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A Lightweight and High Performance Monolingual Word Aligner

A Lightweight and High Performance Monolingual Word Aligner. Xuchen Yao, Benjamin Van Durme, (Johns Hopkins) Chris Callison-Burch and Peter Clark (UPenn) (Vulcan). monolingual word alignment. Aligning one sentence pair from RTE2

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A Lightweight and High Performance Monolingual Word Aligner

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  1. A Lightweight and High Performance Monolingual Word Aligner Xuchen Yao, Benjamin Van Durme, (Johns Hopkins) Chris Callison-Burch and Peter Clark (UPenn) (Vulcan)

  2. monolingual word alignment • Aligning one sentence pair from RTE2 • Premise: Linda Johnson, who lives with her husband, Charles, and two cats in ... , said Katrina has ... • Hypothesis: Linda Johnson is married to Charles • alignment contributed by Brockett (2007) ACL 2013, Sofia

  3. monolingual vs. bilingual aligment • less training data (labeled or unlabeled), but more lexical resources • semantic relatedness: cued by distributional word similaries • the same grammar shared by source/target sentences ACL 2013, Sofia

  4. monolingual vs. bilingual aligment • less training data (labeled or unlabeled), but more lexical resources • semantic relatedness: cued by distributional word similaries • the same grammar shared by source/target sentences ACL 2013, Sofia

  5. monolingual vs. bilingual aligment • less training data (labeled or unlabeled), but more lexical resources • semantic relatedness: cued by distributional word similaries • the same grammar shared by source/target sentences ACL 2013, Sofia

  6. a discriminative model • first proposed by Blunsom and Cohn (2006): • s, t: source (observation), target sentence • a: target word indices (0 to target length), state 0 is NULL state for deletion. • f(): feature functions ACL 2013, Sofia

  7. a discriminative model • first proposed by Blunsom and Cohn (2006): • s, t: source (observation), target sentence • a: target word indices (0 to target length), state 0 is NULL state for deletion. • f(): feature functions ACL 2013, Sofia

  8. a discriminative model • first proposed by Blunsom and Cohn (2006): • s, t: source (observation), target sentence • a: target word indices (0 to target length), state 0 is NULL state for deletion. • f(): feature functions ACL 2013, Sofia

  9. ACL 2013, Sofia

  10. desired Viterbi decoding path ACL 2013, Sofia

  11. a discriminative model • first proposed by Blunsom and Cohn (2006): • s, t: source (observation), target sentence • a: target word indices (0 to target length), state 0 is NULL state for deletion. • f(): feature functions ACL 2013, Sofia

  12. features • string similarity • Jaro Winkler, Dice Sorensen, Hamming, Jaccard, Levenshtein, NGram overlapping and common prefix matching • POS tags matching • WordNet • hypernym, hyponym, synonym, derived form, entailing, causing, members of, have member, substances of, have substances, parts of, have part ACL 2013, Sofia

  13. features • string similarity • Jaro Winkler, Dice Sorensen, Hamming, Jaccard, Levenshtein, NGram overlapping and common prefix matching • POS tags matching • WordNet • hypernym, hyponym, synonym, derived form, entailing, causing, members of, have member, substances of, have substances, parts of, have part ACL 2013, Sofia

  14. features • string similarity • Jaro Winkler, Dice Sorensen, Hamming, Jaccard, Levenshtein, NGram overlapping and common prefix matching • POS tags matching • WordNet • hypernym, hyponym, synonym, derived form, entailing, causing, members of, have member, substances of, have substances, parts of, have part ACL 2013, Sofia

  15. features • positional • offset difference between src/tgt word • context • whether neighboring words are similar • helps to align functional words • distortion (Markov feature) • how far apart are two aligned target words ACL 2013, Sofia

  16. features • positional • offset difference between src/tgt word • context • whether neighboring words are similar • helps to align functional words • distortion (Markov feature) • how far apart are two aligned target words ACL 2013, Sofia

  17. features • positional • offset difference between src/tgt word • context • whether neighboring words are similar • helps to align functional words • distortion (Markov feature) • how far apart are two aligned target words ACL 2013, Sofia

  18. Implementation: jacana-alignsource code at http://code.google.com/p/jacana • lightweight: only used a POS tagger and WordNet • written in Scala, optimize with LBFGS • platform independent, compiles to a .jar file, fully interoperable with Java • high performance? -> evaluation ACL 2013, Sofia

  19. Baselines • GIZA++ • Tree Edit Distance (with stem/wordnet matching) • MANLI • MacCartney, B.; Galley, M. & Manning, C. D., A Phrase-Based Alignment Model for Natural Language Inference, EMNLP 2008 • MANLI-constraint (decoding with ILP) • Thadani, K. & McKeown, K. Optimal and syntactically-informed decoding for monolingual phrase-based alignment. ACL 2011 ACL 2013, Sofia

  20. Baselines • GIZA++ • Tree Edit Distance (with stem/wordnet matching) • MANLI • MacCartney, B.; Galley, M. & Manning, C. D., A Phrase-Based Alignment Model for Natural Language Inference, EMNLP 2008 • MANLI-constraint (decoding with ILP) • Thadani, K. & McKeown, K. Optimal and syntactically-informed decoding for monolingual phrase-based alignment. ACL 2011 ACL 2013, Sofia

  21. Baselines • GIZA++ • Tree Edit Distance (with stem/wordnet matching) • MANLI • MacCartney, B.; Galley, M. & Manning, C. D., A Phrase-Based Alignment Model for Natural Language Inference, EMNLP 2008 • MANLI-constraint (decoding with ILP) • Thadani, K. & McKeown, K. Optimal and syntactically-informed decoding for monolingual phrase-based alignment. ACL 2011 ACL 2013, Sofia

  22. Baselines • GIZA++ • Tree Edit Distance (with stem/wordnet matching) • MANLI • MacCartney, B.; Galley, M. & Manning, C. D., A Phrase-Based Alignment Model for Natural Language Inference, EMNLP 2008 • MANLI-constraint (decoding with ILP) • Thadani, K. & McKeown, K. Optimal and syntactically-informed decoding for monolingual phrase-based alignment. ACL 2011 ACL 2013, Sofia

  23. 10.3% performance in F1 ACL 2013, Sofia

  24. performance in F1 0.8% 3.3% ACL 2013, Sofia

  25. 20x 20x performance in speed(seconds per sentecne) • when sentences are more balanced, jacana-align is about 20x faster ACL 2013, Sofia

  26. 30x 30x 4x performance in speed(seconds per sentecne) • the speed of jacana-align is not as sensitive to sentence length increase ACL 2013, Sofia

  27. Conclusion • state-of-the-art monolingual word aligner • in accuracy • in speed • open source, use it and hack it! ACL 2013, Sofia

  28. thank youwith a demo

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