Translation Model Adaptation for Statistical Machine Translation with Monolingual Topic Information

To adapt a translation model trained from the data in one domain to another, previous works paid more attention to the studies of parallel corpus while ignoring the in-domain monolingual corpora which can be obtained more easily. In this paper, we propose a novel approach for translation model adaptation by utilizing in-domain monolingual topic information instead of the in-domain bilingual corpora, which incorporates the topic information into translation probability estimation. Our method establishes the relationship between the out-of-domain bilingual corpus and the in-domain monolingual corpora via topic mapping and phrase-topic distribution probability estimation from in-domain monolingual corpora. Experimental result on the NIST Chinese-English translation task shows that our approach significantly outperforms the baseline system.