Domain Adaptation for SMT Using Sentence Weight

We describe a sentence-level domain adaptation translation system, which trained with the sentence-weight model. Our system can take advantage of the domain information in each sentence rather than in the corpus. It is a fine-grained method for domain adaptation. By adding weights which reflect the preference of target domain to the sentences in the training set, we can improve the domain adaptation ability of a translation system. We set up the sentence-weight model depending on the similarity between sentences in the training set and the target domain text. In our method, the similarity is measured by the word frequency distribution. Our experiments on a large-scale Chinese-to-English translation task in news domain validate the effectiveness of our sentence-weight-based adaptation approach, with gains of up to 0.75 BLEU over a non-adapted baseline system.