In this paper the authors present various techniques of how to achieve MT domain adaptation with limited in-domain resources. This paper gives a case study of what works and what not if one has to build a domain specific machine translation system. Systems are adapted using in-domain comparable monolingual and bilingual corpora (crawled from the Web) and bilingual terms and named entities. The authors show how to efficiently integrate terms within statistical machine translation systems, thus significantly improving upon the baseline.
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