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 vi-a 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.
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