1 / 12

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”

kirby
Télécharger la présentation

TIDES MT Workshop Review

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related