Word sense disambiguation for machine translation
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Word Sense Disambiguation for Machine Translation. Han-Bin Chen 2010.11.24. Reference Paper. Cabezas and Resnik . 2005. Using WSD Techniques for Lexical Selection . (Technical report) Carpuat and Wu. 2005. Word Sense Disambiguation vs. Statistical Machine Translation . (ACL 2005)

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Word Sense Disambiguation for Machine Translation

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Word sense disambiguation for machine translation

Word Sense Disambiguation for Machine Translation

Han-Bin Chen

2010.11.24


Reference paper

Reference Paper

  • Cabezas and Resnik. 2005. Using WSD Techniques for Lexical Selection. (Technical report)

  • Carpuat and Wu. 2005. Word Sense Disambiguation vs. Statistical Machine Translation. (ACL 2005)

  • Carpuat and Wu. 2005. Improving Statistical Machine Translation using Word Sense Disambiguation. (EMNLP 2007)

  • Chan et al. 2007. Word Sense Disambiguation Improves Statistical Machine Translation. (ACL 2007)

  • Apidianaki. 2009. Data-driven semantic analysis for multilingual WSD. (EACL 2009)


Smt workflow

SMT Workflow

Bilingual Corpus

Monolingual Corpus

Translation model

Reordering model

Language model

Decoder

Input: source language

Output: target language


Mt research areas

MT Research Areas

Bilingual Corpus

Monolingual Corpus

Word Alignment

Translation model

Reordering model

Language model

Decoder

Input: source language

Output: target language

Evaluation Metric


Translation model tm

Translation Model (TM)

  • Research in TM

    • Phrase extraction

    • Phrase filtering

    • Phrase augmentation

    • Word Sense Disambiguation (WSD)


Traditional wsd

Traditional WSD

  • Target word is a single content word

    • Noun, verb, adjectives

  • Classification task with predefined senses

    • WordNet, HowNet

  • Modern WSD system

    • Not limited to local context

    • Linguistic information

    • Position-sensitive

    • Syntactic

    • Collocation

  • A intuitive application of WSD is SMT


Wsd in mt

WSD in MT

  • Wrong translations from Google Translate

  • what is today's special ?

  • 什 麼 是 今 天 的 特 色?

  • I would like to reserve a table for three

  • 我想保留一表三

  • the plane will briefly stop over in the airport

  • 這架飛機將簡要地停留在機場


Wsd in mt early stage

WSD in MT: Early Stage

  • Whether WSD model can help SMT

    • Energetically debated question over the past years

  • Implicit WSD in SMT

    • Local context: phrase table & language model

  • Dedicated WSD system

    • Wider variety of context features

    • Position, sentence-level, document-level features

  • WSD should play a role in MT

  • Publicly available SMT system

    • Pharaoh by Philipp Koehn (2003~2004)


Small scale experiment 1

Small Scale Experiment (1)

  • Marine CARPUAT and Dekai Wu, 2005

  • Chinese-to-English translation task

  • Chinese lexical sample task includes 20 target

  • Trained with state-of-the-art WSD

    • 37 training instances per target word

(manual annotation)


Small scale experiment 2

Small Scale Experiment (2)

  • Hard decision

    • Force the decoder to choose translations from glosses

    • Decided by language model

  • Surprising and frustrating result

    • Small data, out-of-domain material, hard decision

    • Language model effect


Translation disambiguation 1

Translation Disambiguation (1)

  • Clara Cabezas and Philip Resnik, 2005

    • Address 3 problems of the previous work

  • Use aligned target word directly as "sense"

    • 4 senses for "briefly": {短暫地, 短時間地, 簡潔地, 簡要地}

    • Trained with state-of-the-art WSD

    • Handle "small data" and "out-of-domain" problems

  • Soft decision

    • Pharoah XML markup

      • Choose specified translations and translation model together

    • Handle "hard decision" problem


Translation disambiguation 2

Translation Disambiguation (2)

  • Pharaoh XML markup

  • Experiment & Result

    • Spanish-to-English test from Europarl test

    • WSD: 0.2382, Baseline: 0.2356

      • Not statistically significant

      • But at least it is not a decrease


Toward better integration into smt

Toward Better Integration into SMT

  • How to better integrate WSD into SMT?

  • Phrase-based sense disambiguation (PSD)

  • Key points

    • Phrase, not word

    • Integration into log-linear model: weight tuning


Successful integration 1

Successful Integration (1)

  • Chan et al., 2007

  • Chinese-to-English translation

  • Sense disambiguation on Chinese phrase

    • 1 or 2 consecutive Chinese words

    • Extract training examples from word-aligned corpus

  • Add WSD features

    • Contextual probability of WSD

    • Reward probability of WSD


Successful integration 2

Successful Integration (2)

  • Statistically significant improvement

  • 將 無法 取得 更 多 援助 或 其他 讓步

  • Hiero: will be more aid and other concessions

  • Hiero+WSD: will be unable to obtain more aid and other concessions


Psd system 1

PSD System (1)

  • Marine CARPUAT and Dekai Wu, 2007

  • WSD model for every phrase

    • Extract training data from phrase extraction

    • WSD probability as new feature

  • Comments

    • Not every phrase need WSD

    • Technical problem (Pharaoh)


Psd system 2

PSD System (2)

  • Result: better translation on all test sets

IWSLT 2006 dataset

NIST 2004 test set


Psd system 3

PSD System (3)


Recent issue

Recent Issue

  • Different translations may have the same sense

    • 2 senses for "briefly", rather than 4

    • Sense 1: {短暫地, 短時間地}

    • Sense 2: {簡潔地, 簡要地}

  • Automatic sense clustering


Sense clustering 1

Sense Clustering (1)

  • Marianna Apidianaki, 2009

  • Two translations are semantically related

    • If they occur in similar context

  • Translation unit (TU) as context

    • Bilingual sentence pair

  • Source word "briefly"

  • Translations

    • {短暫地, 短時間地, 簡潔地, 簡要地}

    • {t1, t2, t3, t4}


Sense clustering 2

Sense Clustering (2)

  • "briefly-t1" occurs in context {TU1, TU4, TU25, TU88…}

  • "briefly-t2" occurs in context {TU5, TU18, TU92, TU126…}

  • Clustering based on pairwise context similarity

    • Apidianaki, 2008


Sense clustering 3

Sense Clustering (3)

  • Experiment

    • English-Greek translation

    • 150 ambiguous English nouns

  • Evaluation of lexical selection

    • Strict precision (Exact match with answer word)

    • Enriched precision (Match with the cluster of answer word)

  • Result


Conclusion

Conclusion

  • From WSD to PSD

  • However, semantic is also important

  • Future work

    • Semantic PSD


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