Unsupervised Translation Disambiguation for Cross-Domain Statistical Machine Translation

Most attempts at integrating word sense disambiguation with statistical machine translation have focused on supervised disambiguation approaches. These approaches are of limited use when the distribution of the test data differs strongly from that of the training data; however, word sense errors tend to be especially common under these conditions. In this paper we present different approaches to unsupervised word translation disambiguation and apply them to the problem of translating conversational speech under resourcepoor training conditions. Both human and automatic evaluation metrics demonstrate significant improvements resulting from our technique.