LING073 - User contributions [en]

Dhivehi and English/Final project

2019-05-15T02:02:35Z

Nmaini1: /* Improving the RBMT system */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model two's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html Transformer] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php Translation Services]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Future Work==

====Improving the RBMT system====
:* Implement more grammatical structures including:
::# Honorific System
::# Verb Tenses and Forms
::# Pronouns

====Improving the NMT system====
:* Tweak hyper-parameters associated with the neural net such as learning rate, number of hidden layers, and others
:* Expand size of parallel corpus

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
[http://wiki.apertium.org/wiki/Main_Page Apertium]

[https://opennmt.net OpenNMT]

[http://en.wikipedia.org/wiki/Neural_machine_translation Neural Machine Translation]

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T02:02:17Z

Nmaini1: /* Future Work */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model two's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html Transformer] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php Translation Services]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Future Work==

====Improving the RBMT system====
:* Implement more grammatical structures
::# Honorific System
::# Verb Tenses and Forms
::# Pronouns

====Improving the NMT system====
:* Tweak hyper-parameters associated with the neural net such as learning rate, number of hidden layers, and others
:* Expand size of parallel corpus

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
[http://wiki.apertium.org/wiki/Main_Page Apertium]

[https://opennmt.net OpenNMT]

[http://en.wikipedia.org/wiki/Neural_machine_translation Neural Machine Translation]

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:56:10Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model two's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html Transformer] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php Translation Services]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Future Work==

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
[http://wiki.apertium.org/wiki/Main_Page Apertium]

[https://opennmt.net OpenNMT]

[http://en.wikipedia.org/wiki/Neural_machine_translation Neural Machine Translation]

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:55:14Z

Nmaini1: /* Cross Validation for NMT */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model two's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html Transformer] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php Translation Services]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
[http://wiki.apertium.org/wiki/Main_Page Apertium]

[https://opennmt.net OpenNMT]

[http://en.wikipedia.org/wiki/Neural_machine_translation Neural Machine Translation]

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:53:29Z

Nmaini1: /* References */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model 2's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html Transformer] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php Translation Services]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
[http://wiki.apertium.org/wiki/Main_Page Apertium]

[https://opennmt.net OpenNMT]

[http://en.wikipedia.org/wiki/Neural_machine_translation Neural Machine Translation]

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:52:54Z

Nmaini1: /* Evaluation */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model 2's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html Transformer] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php Translation Services]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - https://opennmt.net

NMT - http://en.wikipedia.org/wiki/Neural_machine_translation

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:51:41Z

Nmaini1: /* References */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model 2's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the Transformer [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - https://opennmt.net

NMT - http://en.wikipedia.org/wiki/Neural_machine_translation

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:48:12Z

Nmaini1: /* References */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model 2's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the Transformer [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - https://opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:38:17Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower. We can note that model 2's prediction feature more words overall than the other two models.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the Transformer [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:36:32Z

Nmaini1: /* Meaning of Results */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the Transformer [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through [https://www.translation-services-usa.com/languages/dhivehi.php]) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:35:41Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the Transformer [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through https://www.translation-services-usa.com/languages/dhivehi.php) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

[[Category:Sp19 FinalProjects]]

Dhivehi and English/Final project

2019-05-15T01:35:08Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the Transformer [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through https://www.translation-services-usa.com/languages/dhivehi.php) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

[Category:Sp19 FinalProjects]

Dhivehi and English/Final project

2019-05-15T01:33:56Z

Nmaini1: /* RBMT and NMT */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the Transformer [http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through https://www.translation-services-usa.com/languages/dhivehi.php) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

Dhivehi and English/Final project

2019-05-15T01:33:31Z

Nmaini1: /* RBMT and NMT */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model (the Transformer[http://opennmt.net/OpenNMT-tf/package/opennmt.models.transformer.html] model) that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through https://www.translation-services-usa.com/languages/dhivehi.php) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

Dhivehi and English/Final project

2019-05-15T01:32:23Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation for NMT====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

====Meaning of Results====

These results tell us that the NMT implementation is very promising. If we were able to find a relatively inexpensive translator (perhaps through https://www.translation-services-usa.com/languages/dhivehi.php) and be able to build a large parallel corpus consisting of more modernized phrases, then we could have a more effective "modern-day" NMT system. The RBMT is more adaptable to modernized texts as we can tweak the individual mappings of words and grammar rules.

In short, if we had a large parallel corpus of modern text, then the NMT implementation would likely provide decent translation given enough training time.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

Dhivehi and English/Final project

2019-05-15T01:27:29Z

Nmaini1: /* Cross Validation */

Dhivehi and English/Final project

2019-05-15T01:23:49Z

Nmaini1: /* Dhivehi RBMT and ONMT */

Dhivehi and English/Final project

2019-05-15T01:23:41Z

Nmaini1: /* Dhivehi RBMT and ONMT */

== Dhivehi RBMT and ONMT==

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==Evaluation==

====Cross Validation====

{| class="wikitable"
|-
! Fold
! WER
! PER
! Words in target
! Words in prediction
|-
| '''1'''
| 86.68%
| 40.49%
| 12970
| 9650
|-
| '''2'''
| 90.69%
| 29.38%
| 12664
| 12070
|-
| '''3'''
| 85.65%
| 48.29%
| 12292
| 7802
|}

We used three-fold cross validation and found that the second model performed the best against its corresponding validation data set. We deemed it the best because the WER's of all the models were somewhat similar, but model two's PER was significantly lower.

====RBMT and NMT====
{| class="wikitable"
|-
!System
!WER
!PER
!Words in target
!Words in prediction
|-
|'''RBMT'''
| 94.77%
| 89.37%
| 12211
| 10125
|-
|'''NMT'''
| 88.17%
| 32.02%
| 12211
| 10629
|}

We found that an NMT model that trained for approximately thirty-nine hours performed better in-terms of WER and PER than the RBMT we worked on this semester. We must note, however, the data pooled together for training, validating, and testing the NMT system consisted primarily of biblical text. So, it is likely that the majority of the test data was from the Bible.

==Github==

You can find the code for this project available at: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

Dhivehi and English/Final project

2019-05-15T01:20:50Z

Nmaini1: /* RBMT and NMT */

Dhivehi and English/Final project

2019-05-15T01:18:47Z

Nmaini1: /* RBMT and NMT */

Dhivehi and English/Final project

2019-05-15T01:18:30Z

Nmaini1: /* Dhivehi RBMT and ONMT */

Dhivehi and English/Final project

2019-05-15T01:13:55Z

Nmaini1: /* Evaluation */

Dhivehi and English/Final project

2019-05-15T01:13:34Z

Nmaini1: /* Evaluation */

Dhivehi and English/Final project

2019-05-15T01:10:31Z

Nmaini1: /* Dhivehi RBMT and ONMT */

Dhivehi and English/Final project

2019-05-15T01:08:39Z

Nmaini1: /* Dhivehi RBMT and ONMT */

Dhivehi and English/Final project

2019-05-15T01:07:51Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

* GitHub: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page

OpenNMT - opennmt.net

NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

Dhivehi and English/Final project

2019-05-15T01:07:33Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

* GitHub: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights. The neural net learns the features of Dhivehi based on input and tries to predict the correct English sequences.

==References==
Apertium - http://wiki.apertium.org/wiki/Main_Page
OpenNMT - opennmt.net
NMT - https://en.wikipedia.org/wiki/Neural_machine_translation

Dhivehi and English/Final project

2019-05-15T00:45:20Z

Nmaini1: /* Dhivehi RBMT and ONMT */

Dhivehi and English/Final project

2019-05-15T00:40:05Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

* GitHub: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi and wrote various rules (phonological, syntactic, etc.) that map Dhivehi to English.

===NMT===
Neural Machine Translation: using OpenNMT, we trained on Dhivehi-English parallel corpus consisting of mostly biblical text, some modern phrases, and Universal Declaration of Human Rights.
The neural net learns the features of Dhivehi based on input and
tries to predict the correct English sequences.

Dhivehi and English/Final project

2019-05-15T00:29:44Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

* GitHub: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

===RBMT===
Using Apertium, we studied the grammatical structure of Dhivehi
and wrote various rules (phonological, syntactic, etc.) that map
Dhivehi to English.

===NMT===

Dhivehi and English/Final project

2019-05-15T00:29:21Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

* GitHub: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

==Background==

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

==Motivation/Goal==

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

==Methods==

Using Apertium, we studied the grammatical structure of Dhivehi
and wrote various rules (phonological, syntactic, etc.) that map
Dhivehi to English.

Dhivehi and English/Final project

2019-05-15T00:28:08Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

* GitHub: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

Dhivehi is a language with limited technological resources. Currently there are online Dhivehi-English dictionaries and various keyboard layouts. However, the dictionaries are not comprehensive. There is no existing machine translation system for Dhivehi to English. For this project, we explored two approaches to Dhivehi-English machine translation - rule-based machine translation and neural machine translation.

Building a robust Dhivehi-English machine translation system would be beneficial to the Dhivehi language community by connecting them to the English language through technology. In addition, it will be beneficial for language learner of Dhivehi or English. It also promotes Dhivehi. Our goal is to explore rule-based machine translation and neural machine translation for Dhivehi-English. We will examine the advantages and disadvantages of both approaches.

Dhivehi and English/Final project

2019-05-14T23:45:53Z

Nmaini1: /* Dhivehi RBMT and ONMT */

= Dhivehi RBMT and ONMT=

* GitHub: https://github.com/nav6maini/Dhivehi-RBMT-and-ONMT

Dhivehi and English/Contrastive Grammar

2019-04-16T12:51:19Z

Nmaini1: /* SOV Word Order */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}} }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow{{tag|adv}} Ali{{tag|np}} will clean{{tag|vblex}}{{tag|inf}} the{{tag|det}}{{tag|def}}{{tag|sp}} bathroom{{tag|n}}{{tag|sg}}.{{tag|sent}}}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}|Where{{tag|adv}}{{tag|itg}} is{{tag|vbser}}{{tag|pri}}{{tag|p3}}{{tag|sg}} the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}}އެވެ{{tag|mod}} މާ{{tag|adj}} ވުރެ{{tag|post}} ގެ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dat}} މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The school is much bigger than the house.}}
* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}


=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T12:51:00Z

Nmaini1: /* Sentence Final -އެވެ */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow{{tag|adv}} Ali{{tag|np}} will clean{{tag|vblex}}{{tag|inf}} the{{tag|det}}{{tag|def}}{{tag|sp}} bathroom{{tag|n}}{{tag|sg}}.{{tag|sent}}}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}|Where{{tag|adv}}{{tag|itg}} is{{tag|vbser}}{{tag|pri}}{{tag|p3}}{{tag|sg}} the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}}އެވެ{{tag|mod}} މާ{{tag|adj}} ވުރެ{{tag|post}} ގެ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dat}} މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The school is much bigger than the house.}}
* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}


=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T12:39:15Z

Nmaini1: /* Human/Non-human Distinction */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}
* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}


=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T12:38:46Z

Nmaini1: /* No explicit "be" (except in Noun-Noun Sentences) */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}
* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}


=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T12:37:37Z

Nmaini1: /* SOV Word Order */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}
* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}


=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T12:35:11Z

Nmaini1: /* Sentence Final -އެވެ */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}
* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}


=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T12:34:54Z

Nmaini1: /* Sentence Final -އެވެ */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}


=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T12:33:03Z

Nmaini1: /* Future Tense Marked with Suffix */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T09:32:14Z

Nmaini1: /* SOV Word Order */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T09:29:21Z

Nmaini1: /* Future Tense Marked with Suffix */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

not sure what the following is...
މާދަމާ{{tag|adv}} އަލީ{{tag|np}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ސ ކުރާނެ.

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T09:29:02Z

Nmaini1: /* Future Tense Marked with Suffix */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

not sure what the following is...
މާދަމާ{{tag|adv}} އަލީ{{tag|np}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ސ ކުރާނެ.

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މުބާރާތް މިއަދު ނިމޭނެއެވެ..|The competition will end today.}}
*:{{transferMorphTest|div|eng|.{{tag|sent}} އެވެ{{tag|mod}}ނިމެ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}}/end{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|fut}}{{tag|p3}} މިއަދު{{tag|adv}}/today{{tag|adv}} މުބާރާތް{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/competition{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}.|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T09:18:43Z

Nmaini1: /* Sentence Final -އެވެ */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

not sure what the following is...
މާދަމާ{{tag|adv}} އަލީ{{tag|np}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ސ ކުރާނެ.

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|..{{tag|sent}} އެވެ{{tag|mod}}+ފަ{{tag|succ}}+ދުކޮށްލާ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|pprs}} ވަނީ{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}} އެމީހުން{{tag|prn}}{{tag|pers}}{{tag|p3}}{{tag|pl}}{{tag|dst}}{{tag|dir}} ނުވަނީސް{{tag|adv}} އިރު{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ގަޑި 24 {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އެކަމަކު{{tag|cnjcoo}}|But they were released within 24 hours}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T07:53:05Z

Nmaini1: /* Sentence Final -އެވެ */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

not sure what the following is...
މާދަމާ{{tag|adv}} އަލީ{{tag|np}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ސ ކުރާނެ.

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T07:50:57Z

Nmaini1: /* Human/Non-human Distinction */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

not sure what the following is...
މާދަމާ{{tag|adv}} އަލީ{{tag|np}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ސ ކުރާނެ.

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ

fff{{lang|dv|ބޮޑެވެ}}{{tag|4}} ffff{{lang|dv|މާ}}{{tag|3}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T07:50:27Z

Nmaini1: /* Human/Non-human Distinction */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

not sure what the following is...
މާދަމާ{{tag|adv}} އަލީ{{tag|np}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ސ ކުރާނެ.

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}
|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}
|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ

fff{{lang|dv|ބޮޑެވެ}}{{tag|4}} ffff{{lang|dv|މާ}}{{tag|3}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T07:50:14Z

Nmaini1: /* Human/Non-human Distinction */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

not sure what the following is...
މާދަމާ{{tag|adv}} އަލީ{{tag|np}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ސ ކުރާނެ.

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}
|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ {{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/improvement{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}
|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ

fff{{lang|dv|ބޮޑެވެ}}{{tag|4}} ffff{{lang|dv|މާ}}{{tag|3}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]

Dhivehi and English/Contrastive Grammar

2019-04-16T07:49:36Z

Nmaini1: /* Human/Non-human Distinction */

= div-eng tests =

=== SOV Word Order ===

* {{transferTest|div|eng|!އަހަރެން އެކަނި ދުކޮހް ލާ|Leave me alone!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}}ލާ{{tag|mod}} ދުކޮހް{{tag|v}}{{tag|tv}}{{tag|act}}{{tag|pres}}{{tag|p3}}{{tag|imp}} އެކަނި{{tag|adj}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}| Leave{{tag|vblex}}{{tag|inf}} me{{tag|prn}}{{tag|obj}}{{tag|p1}}{{tag|mf}}{{tag|sg}} alone{{tag|adv}}! }}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferTest|div|eng|.{{tag|sent}} ކުރާނެ{{tag|final}} ސާފު{{tag|adj}}/clean{{tag|adj}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} އަލީ{{tag|np}} މާދަމާ{{tag|adv}}|Tomorrow Ali will clean the bathroom.}}

not sure what the following is...
މާދަމާ{{tag|adv}} އަލީ{{tag|np}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ސ ކުރާނެ.

=== No explicit "be" (except in Noun-Noun Sentences) ===

* {{transferTest|div|eng|ކޮބާ ފާހަނަ؟|Where is the bathroom?}}
*:{{transferMorphTest|div|eng|؟{{tag|sent}} ފާހަނަ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}} ކޮބާ{{tag|itg}}\where{{tag|itg}}|Where is the bathroom?}}

* {{transferTest|div|eng|އަހަރެން ފެތެނީ!|I am sinking!}}
*:{{transferMorphTest|div|eng|!{{tag|sent}} ފެތެނީ{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}}/sink{{tag|v}}{{tag|iv}}{{tag|pass}}{{tag|pprs}} އަހަރެން{{tag|prn}}{{tag|pers}}{{tag|p1}}{{tag|sg}}{{tag|std}}{{tag|dir}}|I am sinking!}}

=== Human/Non-human Distinction ===

* {{transferTest|div|eng|މީހާ|person}}
*:{{transferMorphTest|div|eng|މީހާ{{tag|n}}{{tag|hum}}{{tag|sg}}{{tag|def}}{{tag|dir}}|person}}

* {{transferTest|div|eng|މަދްރަސާ|school}}
*:{{transferMorphTest|div|eng|މަދްރަސާ{{tag|n}}{{tag|nhum}}{{tag|sg}}{{tag|def}}{{tag|dir}}/school{{tag|n}}{{tag|sg}}{{tag|def}}{{tag|dir}}
|school}}

* {{transferTest|div|eng|ތަރައްގީ|improvement}}
*:{{transferMorphTest|div|eng|ތަރައްގީ|improvement}}

=== Sentence Final -އެވެ ===

* {{transferTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.|The school is much bigger than the house.}}
*:{{transferMorphTest|div|eng|މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ.&rlm; {{tag|none}}|The school is much bigger than the house.}}

މަދްރަސާ ގެއަށްވުރެ މާ ބޮޑެވެ

fff{{lang|dv|ބޮޑެވެ}}{{tag|4}} ffff{{lang|dv|މާ}}{{tag|3}}

* {{transferTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}
*:{{transferMorphTest|div|eng|އެކަމަކު 24 ގަޑި އިރު ނުވަނީސް އެމީހުން ވަނީ ދުކޮށްލާފައެވެ.|But they were released within 24 hours.}}

general format:
{{lang|fa|حسن صباح}} &lrm;(1090–1124)

=== Future Tense Marked with Suffix ===

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|The competition will end today.}}

* {{transferTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}
*:{{transferMorphTest|div|eng|މާދަމާ އަލީ ފާހަނަ ސާފު ކުރާނެ.|Tomorrow Ali will clean the bathroom.}}

[[Category:Dhivehi]]
[[Category:English]]
[[Category:Sp19_ContrastiveGrammars]]