Novel Document Level Features for Statistical Machine Translation

In this paper, we introduce document level features that capture necessary information to help MT system perform better word sense disambiguation in the translation process. We describe enhancements to a Maximum Entropy based translation model, utilizing long distance contextual features identified from the span of entire document and from both source and target sides, to improve the likelihood of the correct translation for words with multiple meanings, and to improve the consistency of the translation output in a document setting. The proposed features have been observed to achieve substantial improvement of MT performance on a variety of standard test sets in terms of TER/BLEU score.