Translation model adaptation for statistical machine translation with monolingual topic information

  • Authors:

  • Jinsong Su;Hua Wu;Haifeng Wang;Yidong Chen;Xiaodong Shi;Huailin Dong;Qun Liu

  • Affiliations:

  • Xiamen University, Xiamen, China;Baidu Inc., Beijing, China;Baidu Inc., Beijing, China;Xiamen University, Xiamen, China;Xiamen University, Xiamen, China;Xiamen University, Xiamen, China;Chinese Academy of Sciences, Beijing, China

  • Venue:
  • ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
  • Year:
  • 2012

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Abstract

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.