Mixture-modeling with unsupervised clusters for domain adaptation in statistical machine translation
Rico Sennrich · Edinburgh Research Explorer (University of Edinburgh) · 2012
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.