Effective Use of Linguistic and Contextual Informationfor Statistical Machine

TranslationLibin Shen and Jinxi Xu and Bing Zhang and

Spyros Matsoukas and RalphWeischedelBBN Technologies

EMNLP2009Presented by Cai

Question

Lexical features are useful in MT But parameter’s number is large How to effectively use these features?

Previous Work

Discriminative training the parameters : the need of scalable development set and careful selection

Estimate a single score or likelihood of a translation with rich features (using ME): feature space too large, not practical

Main Contribution

Design effective and efficient statistical models (simple probabilistic models) to capture useful linguistic and context information for MT decoding

Features: robust and ideal

Features introduced

non-terminal labels (+performance) Length distribution of non-terminals

(+performance) Source-side context information

(+performance) Source-side structural information

(dependency information) no performance gain, surprisingly

What’s special

Assume the distribution of length of non-terminal is Gaussian (sampling,estimation, smoothing)

Soft dependency constraints by introducing labels of non-terminals

Context language model String-to-dependency rule-> dependency-to-

dependency rule

Experiments

Baseline: string-to-dependency system presented in (Shen et.al 2008)

Test each feature and their combinations Arabic-to-English and Chinese-to-English Measure: Bleu and TER Results: 2 points of BLEU in A-E and 1 points of B

LEU in C-E (nist06); 1.7 points of BLEU in A-E and 0.8 points of BLEU in C-E (nist06); 1.7 poi

Main Related Work

Z. He, Q. Liu, and S. Lin. 2008. Improving statistical machine translation using lexicalized rule, COLING ’08

A. Ittycheriah and S. Roukos. 2007. Direct translation model 2. NACCL 07

L. Shen, J. Xu, and R. Weischedel. 2008. A New String-to-Dependency Machine Translation Algorithm with a Target Dependency Language Model. ACL 2008