Paper: A Sense-Based Translation Model for Statistical Machine Translation

ACL ID P14-1137
Title A Sense-Based Translation Model for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

The sense in which a word is used deter- mines the translation of the word. In this paper, we propose a sense-based transla- tion model to integrate word senses into statistical machine translation. We build a broad-coverage sense tagger based on a nonparametric Bayesian topic model that automatically learns sense clusters for words in the source language. The pro- posed sense-based translation model en- ables the decoder to select appropriate translations for source words according to the inferred senses for these words us- ing maximum entropy classifiers. Our method is significantly different from pre- vious word sense disambiguation reformu- lated for machine translation in that the lat- ter neglects word senses in nature. We test the effectiveness of the proposed sense- based translati...