Paper: Improving Statistical Machine Translation Using Word Sense Disambiguation

ACL ID D07-1007
Title Improving Statistical Machine Translation Using Word Sense Disambiguation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We show for the first time that incorporating the predictions of a word sense disambigua- tion system within a typical phrase-based statistical machine translation (SMT) model consistently improves translation quality across all three different IWSLT Chinese- English test sets, as well as producing sta- tistically significant improvements on the larger NIST Chinese-English MT task— and moreover never hurts performance on any test set, according not only to BLEU but to all eight most commonly used au- tomatic evaluation metrics. Recent work has challenged the assumption that word sense disambiguation (WSD) systems are useful for SMT. Yet SMT translation qual- ity still obviously suffers from inaccurate lexical choice. In this paper, we address this problem by investigating a new strat- eg...