Paper: Bilingual Sense Similarity for Statistical Machine Translation

ACL ID P10-1086
Title Bilingual Sense Similarity for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

This paper proposes new algorithms to com- pute the sense similarity between two units (words, phrases, rules, etc.) from parallel cor- pora. The sense similarity scores are computed by using the vector space model. We then ap- ply the algorithms to statistical machine trans- lation by computing the sense similarity be- tween the source and target side of translation rule pairs. Similarity scores are used as addi- tional features of the translation model to im- prove translation performance. Significant im- provements are obtained over a state-of-the-art hierarchical phrase-based machine translation system.