Unsupervised Adaptation for Statistical Machine Translation

In this work, we tackle the problem of language and translation models domain-adaptation without explicit bilingual in-domain training data. In such a scenario, the only information about the domain can be induced from the source-language test corpus. We explore unsupervised adaptation, where the source-language test corpus is combined with the correspond-ing hypotheses generated by the transla-tion system to perform adaptation. We compare unsupervised adaptation to su-pervised and pseudo supervised adapta-tion. Our results show that the choice of the adaptation (target) set is crucial for successful application of adaptation meth-ods. Evaluation is conducted over the German-to-English WMT newswire trans-lation task. The experiments show that the unsupervised adaptation method generates the best translation quality as well as gen-eralizes well to unseen test sets.