Using Topic Models in Domain Adaptation

an important factor of a corpus is its domain, usually the quality of a SMT system trained on an in-domain corpus increases by adding out-of-domain sentences to its training corpus. In this paper we have shown out-of-domain corpora may also contains sentences which are proper for improving the quality of in-domain corpus. These sentences have words and phrases that occur in in-domain corpora so, their context is more similar to the context of in-domain parallel corpus and is far from context of out-of-domain parallel corpora. In this paper we suggest a method based on topic models to extract some sentences from out-of-domain parallel corpora that their context are similar to in-domain parallel corpus. We used these extracted sentences for training an SMT system. Finally, we will show the BLEU score of the system output increases about 4.69% by adding these extra information to its training corpus.