A sense-based translation model for statistical machine translation

The sense in which a word is used determines the translation of the word. In this paper, we propose a sense-based translation 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 proposed sense-based translation model enables the decoder to select appropriate translations for source words according to the inferred senses for these words using maximum entropy classifiers. Our method is significantly different from previous word sense disambiguation reformulated for machine translation in that the latter neglects word senses in nature. We test the effectiveness of the proposed sensebased translation model on a large-scale Chinese-to-English translation task. Results show that the proposed model substantially outperforms not only the baseline but also the previous reformulated word sense disambiguation.