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 corresponding hypotheses generated by the translation system to perform adaptation. We compare unsupervised adaptation to supervised and pseudo supervised adaptation. Our results show that the choice of the adaptation (target) set is crucial for successful application of adaptation methods. Evaluation is conducted over the German-to-English WMT newswire translation task. The experiments show that the unsupervised adaptation method generates the best translation quality as well as generalizes well to unseen test sets.
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