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TIDES MT Workshop Review

TIDES MT Workshop Review. Using Syntax?. ISI-small: Cross-lingual parsing/decoding Input: Chinese sentence + English lattice built with all possible phrase substitutions Output English parse tree Algorithm: lattice parsing (?) JHU: Failed to incorporate “linguistic knowledge”

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TIDES MT Workshop Review

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  1. TIDES MT Workshop Review

  2. Using Syntax? • ISI-small: • Cross-lingual parsing/decoding • Input: Chinese sentence + English lattice built with all possible phrase substitutions • Output English parse tree • Algorithm: lattice parsing (?) • JHU: • Failed to incorporate “linguistic knowledge” • Morphology, NE, Syntax • Modeling phrase movement did not help

  3. Phrase Alignment

  4. ITC-irst • ITC-irst: very similar to Franz’s system • Log-linear model / minimum error training • Phrase based model • Preprocessing • Chinese numberical translation, segmentation, split of long sentence (testing) • LM Adaptation: mixture of LM from different copora

  5. JHU • Alignment template + WFST • Multiple phrase segmentation of source sentence is essential in translation • BiText chunking(DP, and Divisive Clustering): similar idea as our sentence splitting • Phrase-level movement • Document-specific LM (LM adaptation) • Gains from Doc-specific LMs and BMR-Bleu are not additive

  6. ATR • ATR: • unsupervised Chinese word segmenter • Truecasing by Conditional Random Field

  7. IBM • Word reordering • Pre-ordering: reorder the source sentence 10% improvement • Word-level and block level reordering

  8. ISI • Franz system: • Log linear model • Alignment template • Discriminative training • DP search • New feature functions • Lexicalized reordering +1% Bleu • Penalize word deletions +2% Bleu • Tight integration of rule-based translations +2% • Translation Components: numbers, NE, dates • Train classifier to identify where TC works where

  9. ISI • Franz: important things in system developing • Good engineering is important • Scalability • Efficiency • No bugs in software • Good overall system architecture • Error analysis should drive research • Step1: what is the major error in current system • Step2: fix it! • Step3: goto step 1

  10. Comparable Corpora • ISI: • Arabic: 99M->106M; Bleu: 43.8->42.99 • Chinese: 168M->176M; Bleu: 32.05->32.85

  11. Confidence Intervals • Bootstrapping • IBM’s method • Chop the test data into 50 pieces • NIST’s method • Sign test

  12. New Players • BYU: simple transfer system • Linear B: human post edit MT hypothesis (HAMT) • MTM linguaSoft: based on CIMOS rule based system • NTT: WFST based decoder

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