Domain dependent statistical machine translation

While statistical machine translation (SMT) has advanced significantly with better modeling techniques and much more training data, domain specific SMT has received much less attention and leaves much room for further improvements. In this work, we address domain issues and propose to use the combination of feature weights and language model adaptation, to distinguish multiple domains, which share a general translation engine with phrase-based log-linear models. The proposed method requires much less parallel data than what is typically used to build a domain independent system, which makes it easy, cheap and efficient to capture as many domains as required. Domain adaptation during decoding is approached with source text classification methods. Our results on the GALE tasks show significant improvements with the proposed domain dependent translation than domain independent translation. 1