Mixture-Modeling with Unsupervised Clusters for Domain Adaptation in Statistical Machine Translation

Abstract

In Statistical Machine Translation, in-domain and out-of-domain training data are not always clearly delineated. This paper investigates how we can still use mixture-modeling techniques for domain adaptation in such cases. We apply unsupervised clustering methods to split the original training set, and then use mixture-modeling techniques to build a model adapted to a given target domain. We show that this approach improves performance over an unadapted baseline, and several alternative domain adaptation methods.