Search-Aware Tuning for Hierarchical Phrase-based Decoding

Parameter tuning is a key problem for sta-tistical machine translation (SMT). Most popular parameter tuning algorithms for SMT are agnostic of decoding, result-ing in parameters vulnerable to search er-rors in decoding. The recent research of “search-aware tuning ” (Liu and Huang, 2014) addresses this problem by consid-ering the partial derivations in every de-coding step so that the promising ones are more likely to survive the inexact de-coding beam. We extend this approach from phrase-based translation to syntax-based translation by generalizing the eval-uation metrics for partial translations to handle tree-structured derivations in a way inspired by inside-outside algorithm. Our approach is simple to use and can be ap-plied to most of the conventional parame-ter tuning methods as a plugin. Extensive experiments on Chinese-to-English trans-lation show significant BLEU improve-ments on MERT, MIRA and PRO