A Sense-Based Translation Model for Statistical Machine Translation
Deyi Xiong, Min Zhang · 2014
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 translation model on a large-scale Chinese-to-English translation task. Re-sults show that the proposed model sub-stantially outperforms not only the base-line but also the previous reformulated word sense disambiguation. 1