Discriminative Instance Weighting for Domain Adaptation in Statistical Machine Translation

Abstract

We describe a new approach to SMT adapta-tion that weights out-of-domain phrase pairs according to their relevance to the target do-main, determined by both how similar to it they appear to be, and whether they belong to general language or not. This extends previ-ous work on discriminative weighting by us-ing a finer granularity, focusing on the prop-erties of instances rather than corpus com-ponents, and using a simpler training proce-dure. We incorporate instance weighting into a mixture-model framework, and find that it yields consistent improvements over a wide range of baselines. 1