word sense disambiguation for machine translation
Download
Skip this Video
Download Presentation
Word Sense Disambiguation for Machine Translation

Loading in 2 Seconds...

play fullscreen
1 / 23

Word Sense Disambiguation for Machine Translation - PowerPoint PPT Presentation


  • 178 Views
  • Uploaded on

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)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Word Sense Disambiguation for Machine Translation' - moesha


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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

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
ad