Difference between revisions of "Lingala and Kikuyu"
From LING073
(Updated statistics to represent performance after adding tone markings to reference translation) |
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==kik → lin evaluation== | ==kik → lin evaluation== | ||
*Evaluation on mini corpus: | *Evaluation on mini corpus: | ||
| − | **WER: | + | **WER: 80% |
| − | **PER: | + | **PER: 80% |
**Number of words: 7 (This should really be 8, but the command we are supposed to use to count this outputs 7. The proportion remains unchanged in our case, however.) | **Number of words: 7 (This should really be 8, but the command we are supposed to use to count this outputs 7. The proportion remains unchanged in our case, however.) | ||
**Number of unknown words: 0 | **Number of unknown words: 0 | ||
| Line 48: | Line 48: | ||
*Evaluation of corpus: | *Evaluation of corpus: | ||
| − | **WER: | + | **WER: 98.13% |
| − | **PER: | + | **PER: 90.65% |
**Number of words: 106 | **Number of words: 106 | ||
| − | **Number of unknown words: 79 | + | **Number of unknown words: 79 (Note: Using the commands on the wiki gives a slightly different count that using the apertium-eval-translator tool. We use the count given by the command, not the tool) |
**Proportion of stems translated correctly: 25.47% | **Proportion of stems translated correctly: 25.47% | ||
Revision as of 17:30, 26 March 2017
Resources for machine translation between Lingala and Kikuyu. Code is stored in a GitHub Repository.
Contents
Developed Resources
Parallel Corpus
A parallel corpus created using two translations of Genesis is available on Github.
Contrastive Grammar
Contrastive Grammar is detailed on the wiki page Lingala and Kikuyu/Contrastive Grammar.
Lexical Selection
Lexical selection is detailed on the wiki page Lingala and Kikuyu/Lexical Selection.
Lexical Selection
In addition to adding many one-to-one mappings to our .dix file, we have included a couple of one-to-many mappings for each direction of translation. See Lingala and Kikuyu/Lexical Selection for further details.
From Lingala to Kikuyu, we consider four one-to-many mappings:
- The Lingala verb kokíma can be translated in English as both "to run" and "to run away." Kikuyu has two distinct verbs corresponding to these English translations, respectively, hanyũk and ũr.
- The Lingala verb kofánda can be translated in English as both "to sit" and "to live" (as in reside). Kikuyu has two distinct verbs corresponding to these English translations, respectively, ikar and tũũr.
- The Lingala verb kobánza can be translated in English as both "to think" and "to remember." Kikuyu has two distinct verbs corresponding to these English translations, respectively, ĩcir and ririkan.
- The Lingala noun mabele can be translated in English as both "sand" and "milk." Kikuyu has two distinct nouns corresponding to these English translations, respectively, thanga and ria.
From Kikuyu to Lingala, we consider three one-to-many-mappings:
- The Kikuyu verb igu can be translated in English as both "to obey" and "to hear." Lingala has two distinct verbs corresponding to these English translations, respectively, kotosa and koyoka.
- The Kikuyu verb rut can be translated in English as both "to learn" and "to remove." Lingala has two distinct verbs corresponding to these English translations, respectively, koyekola and kokukula.
- the Kikuyu noun ria can be translated in English as both "milk" and "pond." Lingala has two distinct nouns corresponding to these English translations, respectively, mabele and etimá.
lin → kik evaluation
- Evaluation on mini corpus:
- WER: 87.50%
- PER: 87.50%
- Number of words: 9
- Number of unknown words: 0
- Proportion of stems translated correctly: 100%
- Evaluation of corpus:
- WER: 99.06%
- PER: 94.34%
- Number of words: 107
- Number of unknown words: 71
- Proportion of stems translated correctly:33.64%
kik → lin evaluation
- Evaluation on mini corpus:
- WER: 80%
- PER: 80%
- Number of words: 7 (This should really be 8, but the command we are supposed to use to count this outputs 7. The proportion remains unchanged in our case, however.)
- Number of unknown words: 0
- Proportion of stems translated correctly: 100%
- Evaluation of corpus:
- WER: 98.13%
- PER: 90.65%
- Number of words: 106
- Number of unknown words: 79 (Note: Using the commands on the wiki gives a slightly different count that using the apertium-eval-translator tool. We use the count given by the command, not the tool)
- Proportion of stems translated correctly: 25.47%
