Difference between revisions of "User:Francis Tyers/Sandbox2"
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Constraint-based lexical selection for rule-based machine translation |
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==Agenda== |
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<pre> |
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Corpus: cawiki-20110616-pages-articles.xml.bz2 |
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cleaned with `aq-wikicrp' |
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1758582 lines |
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For http://xixona.dlsi.ua.es/freerbmt09/ |
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531983 unique analyses |
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531436 lines with >1 translation (30%) |
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2740 analyses with >1 translation |
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287 words (lemma+pos) with >1 translation in corpus |
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712 words in dictionary with >1 translation |
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1.03 fertility of dictionary over corpus (e.g. total number of word:word translations / total number of words) |
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* Logging on xixona, knowing what people are translating (which language pair etc.) |
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** Possible applications: |
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</pre> |
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** quality control |
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** encourage language pair maintainers |
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** give an idea of missing terms (on a temporal basis? What's in the news?) - getting the information so we can adapt the translators to what people are translating: if certain topics are coming up in the news ('swine flu' etc.), try to catch them |
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Test corpus: |
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* Making a 3.2 release -- x-stage transfer, some changes in lttoolbox |
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* Planning for new releases, apertium 3.4, apertium 4.0? |
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* 150 test words |
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* Webservices -- what, when, where ? |
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* 1,500 sentences |
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* Should we have a concentrated effort on Revo Vortaro import? |
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* 10 per test word |
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** Reta Vortaro is fairly consistent; it has clear delineation between simple, unambiguous terms; terms with more than one possible translation (where the first one listed is the preferred default); and polysemous words. Theres even an XML version |
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* Randomly selected from the subset of sentences which were found in the corpus. |
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*** Who will do the tagging and quality control ? Every bidix item would need to be proofed |
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* Only words with >100 example sentences included |
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* Dix profiling - finding out (on a corpus or on testvoc) how often each entry is used, i.a. for removing unused .dix entries - demo by Jacob |
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* Rationale: Dictionary doesn't provide good enough coverage to produce statistically significant results over a whole corpus. |
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* Discarding because of bad tagging/MWE recognition: 'to', 'sol', 'portada', 'cap', 'cop', 'marxa' (less than 60% correct) |
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Training corpus: |
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Baselines: |
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* TL Frequency-best |
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* TLM-best |
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* Linguist set |
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* Full analysis:Full analysis dic from Giza++ |
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* Rules from phrase table |
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Process for using GIZA++: |
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* Tag both sides of the corpus (europarl, en-ca, first 1,700,000 sentences) with the Apertium language pair. |
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* Extract the model/lex.f2e file. |
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* Take the top scoring analysis:analysis results where the POS matches |
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* Where the word is already ambiguous in the Apertium dictionaries, add the possibilities from GIZA to the dictionary so that they may be chosen -- only added with POS tag. |
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==Annotation process== |
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# Translate corpus (native speaker of English, competent Catalan), adding missing translations to bilingual dictionary options. |
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#* Words with too many tagging errors, or MWE errors are left out. |
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# Proofread corpus |
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# Run corpus up to lexical transfer stage |
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# Annotate output of lexical transfer |
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Latest revision as of 08:11, 30 September 2011
Constraint-based lexical selection for rule-based machine translation
Corpus: cawiki-20110616-pages-articles.xml.bz2
cleaned with `aq-wikicrp'
1758582 lines
531983 unique analyses
531436 lines with >1 translation (30%)
2740 analyses with >1 translation
287 words (lemma+pos) with >1 translation in corpus
712 words in dictionary with >1 translation
1.03 fertility of dictionary over corpus (e.g. total number of word:word translations / total number of words)
Test corpus:
- 150 test words
- 1,500 sentences
- 10 per test word
- Randomly selected from the subset of sentences which were found in the corpus.
- Only words with >100 example sentences included
- Rationale: Dictionary doesn't provide good enough coverage to produce statistically significant results over a whole corpus.
- Discarding because of bad tagging/MWE recognition: 'to', 'sol', 'portada', 'cap', 'cop', 'marxa' (less than 60% correct)
Training corpus:
Baselines:
- TL Frequency-best
- TLM-best
- Linguist set
- Full analysis:Full analysis dic from Giza++
- Rules from phrase table
Process for using GIZA++:
- Tag both sides of the corpus (europarl, en-ca, first 1,700,000 sentences) with the Apertium language pair.
- Extract the model/lex.f2e file.
- Take the top scoring analysis:analysis results where the POS matches
- Where the word is already ambiguous in the Apertium dictionaries, add the possibilities from GIZA to the dictionary so that they may be chosen -- only added with POS tag.
Annotation process[edit]
- Translate corpus (native speaker of English, competent Catalan), adding missing translations to bilingual dictionary options.
- Words with too many tagging errors, or MWE errors are left out.
- Proofread corpus
- Run corpus up to lexical transfer stage
- Annotate output of lexical transfer
