Paper: Translation Model Size Reduction for Hierarchical Phrase-based Statistical Machine Translation

ACL ID P12-2057
Title Translation Model Size Reduction for Hierarchical Phrase-based Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

In this paper, we propose a novel method of reducing the size of translation model for hier- archical phrase-based machine translation sys- tems. Previous approaches try to prune in- frequent entries or unreliable entries based on statistics, but cause a problem of reducing the translation coverage. On the contrary, the pro- posed method try to prune only ineffective entries based on the estimation of the infor- mation redundancy encoded in phrase pairs and hierarchical rules, and thus preserve the search space of SMT decoders as much as possible. Experimental results on Chinese-to- English machine translation tasks show that our method is able to reduce almost the half size of the translation model with very tiny degradation of translation performance.