Word-sense disambiguation for machine translation

In word sense disambiguation, a system attempts to determine the sense of a word from contextual fea-tures. Major barriers to building a high-performing word sense disambiguation system include the dif-ficulty of labeling data for this task and of pre-dicting fine-grained sense distinctions. These is-sues stem partly from the fact that the task is be-ing treated in isolation from possible uses of au-tomatically disambiguated data. In this paper, we consider the related task of word translation, where we wish to determine the correct translation of a word from context. We can use parallel language corpora as a large supply of partially labeled data for this task. We present algorithms for solving the word translation problem and demonstrate a signif-icant improvement over a baseline system. We then show that the word-translation system can be used to improve performance on a simplified machine-translation task and can effectively and accurately prune the set of candidate translations for a word.