Effective Use of Linguistic and Contextual Information for Statistical Machine Translation Libin Shen and Jinxi Xu and Bing Zhang and Spyros Matsoukas and RalphWeischedel BBN Technologies EMNLP2009 Presented 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 BLEU 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 Improving statistical machine translation using lexicalized rule, COLING ’08  A. Ittycheriah and S. Roukos Direct translation model 2. NACCL 07  L. Shen, J. Xu, and R. Weischedel A New String-to-Dependency Machine Translation Algorithm with a Target Dependency Language Model. ACL 2008