Difference between revisions of "Fijian and English"

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(Fijian Transducer)
(Additions)
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==Additions==
 
==Additions==
*100 more stems in the Fijian transducer and the bilingual dictionary (finished adding 100 stems to the transducer; continuing adding them to the bilingual dictionary)
+
*126 more stems in the Fijian transducer and the bilingual dictionary (finished adding 100 stems to the transducer; continuing adding them to the bilingual dictionary)
 
*Morphologies:
 
*Morphologies:
 
:-Causative prefix ''vaka-''
 
:-Causative prefix ''vaka-''

Revision as of 20:45, 27 April 2018

Resources for machine translation between Fijian and English

fij → eng evaluation

Current WER and PER:

Test file: 'fij-eng.tests.txt'
Reference file 'eng.tests.txt'

Statistics about input files
-------------------------------------------------------
Number of words in reference: 56
Number of words in test: 59
Number of unknown words (marked with a star) in test:
Percentage of unknown words: 0.00 %

Results when removing unknown-word marks (stars)
-------------------------------------------------------
Edit distance: 50
Word error rate (WER): 89.29 %
Number of position-independent correct words: 12
Position-independent word error rate (PER): 83.93 %

Results when unknown-word marks (stars) are not removed
-------------------------------------------------------
Edit distance: 50
Word Error Rate (WER): 89.29 %
Number of position-independent correct words: 12
Position-independent word error rate (PER): 83.93 %

Statistics about the translation of unknown words
-------------------------------------------------------
Number of unknown words which were free rides: 0
Percentage of unknown words that were free rides: 0%

eng → fij evaluation

Current WER and PER :

Test file: 'eng-fij.tests.txt'
Reference file 'fij.tests.txt'

Statistics about input files
-------------------------------------------------------
Number of words in reference: 62
Number of words in test: 56
Number of unknown words (marked with a star) in test: 2
Percentage of unknown words: 3.57 %

Results when removing unknown-word marks (stars)
-------------------------------------------------------
Edit distance: 52
Word error rate (WER): 83.87 %
Number of position-independent correct words: 13
Position-independent word error rate (PER): 79.03 %

Results when unknown-word marks (stars) are not removed
-------------------------------------------------------
Edit distance: 52
Word Error Rate (WER): 83.87 %
Number of position-independent correct words: 13
Position-independent word error rate (PER): 79.03 %

Statistics about the translation of unknown words
-------------------------------------------------------
Number of unknown words which were free rides: 0
Percentage of unknown words that were free rides: 0.00 %

Lexical Selection

https://wikis.swarthmore.edu/ling073/Fijian_and_English/Lexical_selection#fij_.E2.86.92_eng

eng → fij one-to-many mapping

  • Case 1: Pelu and lo’i describe two different kinds of bending action.

(eng) bend → (fij) pelu (e.g. bend of metal)

(eng) bend → (fij) lo’i (e.g. bend at a joint)

  • Case 2:

(eng) shine on → (fij) cina (light/torch shines on)

(eng) shine on → (fij) cila (sun/moon/star shines on)

  • (a disambiguation problem) The third person singular pronoun in English does not distinguish between nominative and accusative case.

(eng) it → (fij) e (subj)

(eng) it → (fij) koya (obj)

fij → eng one-to-many mapping

  • Case 1.1:

(fij) yava → (eng) leg

(fij) yava → (eng) foot

  • Case 1.2:

(fij) liga → (eng) arm

(fij) liga → (eng) hand

  • Case 1.3:

(fij) mata → (eng) face

(fij) mata → (eng) eye

  • Case 2:

(fij) vula → (eng) moon

(fij) vula → (eng) month

  • Case 3:

(fij) basu → (eng) tear up (e.g. old clothes)

(fij) basu → (eng) tear down (e.g. old buildings)

  • Case 4:Fijian does not distinguish genders on pronouns.

(fij) koya → (eng) him

(fij) koya → (eng) her

(fij) koya → (eng) it

  • Case 5: (a disambiguation problem?)

The word levu can be used either as an adjective meaning "big", or a number meaning "many, much", but both numbers and adjectives can be a predicate head (like a verb).

(fij) levu → (eng) big (adj)

(fij) levu → (eng) a lot of (num)

Additions

  • 126 more stems in the Fijian transducer and the bilingual dictionary (finished adding 100 stems to the transducer; continuing adding them to the bilingual dictionary)
  • Morphologies:
-Causative prefix vaka-
-Collective prefix vei-

Problems with adding prefixes in the transducer: the form "vakataro" can be correctly analyzed as <vblex><caus>, but the plain form "taro" ('ask') gets two analyses: <vblex><iv> (the correct one) and <vblex><caus>. (Same problem with "vei-".) Besides, the prefix "vaka-" is not always a causative prefix. In fact, attaching to the verb "taro" ('ask'), "vala-" only changes the meaning to 'ask many people' or 'ask many times'.

  • Lexical Selection rules:
-Case 1:

(fij) bale → (eng) fall (fall from a position of standing)

(fij) bale → (eng) die

(fij) bale → (eng) mean

-Case 2: Select 'arrive' as the translation of yaco when the subject NP following the verb is something that can move around; select 'happen' as the translation for yaco when the subject NP is inanimate.

(fij) yaco → (eng) arrive

(fij) yaco → (eng) happen

  • Disambiguation rules:
-Several verbs in Fijian can be used as post-head adverbs, such as oti ('already' or 'finish').
  • Rules: If it follows a verb or an object pronoun, then choose <adv>; if it follows an aspect/tense marker or a subject pronoun, choose <v>.
-The word soqo can be either a verb, meaning 'gather', or a noun, meaning 'meeting'.
  • Rules: If it follows an article, choose <n>; if it follows an aspect/tense marker or a subject pronoun, choose <v>.

Final Evaluation

Fijian Transducer

  • Precision: 92.19219%
  • Recall: 55.11670%
  • Coverage over the large corpus: 70.06%
  • Number of words in the large corpus: 1099762
  • Number of stems in the transducer: 280

MT

fij → eng

  • WER:132.31%
  • PER:123.26%
  • Proportion of correctly translated stems: 16.7%
  • Trimmed coverage over fij.longer.text:45.49%
  • Trimmed coverage over fij.corpus.large:33.5%
  • Number of tokens in longer corpus:1076

eng → fij

  • WER:92.94%
  • PER:85.32%
  • Proportion of correctly translated stems:14.68%
  • Trimmed coverage of eng.longer.txt:47.67%
  • Number of tokens in longer corpus:718.

Contrastive Grammar

https://wikis.swarthmore.edu/ling073/Fijian_and_English/Contrastive_Grammar

Developed Resources for Machine Translation

https://github.swarthmore.edu/hwang11/ling073-fij-eng

https://github.swarthmore.edu/hwang11/ling073-fij-eng-corpus