This paper describes details of the NTT-UT system in NTCIR- 9 PatentMT task. One of its key technology is system com- bination; the final translation hypotheses are chosen from n-bests by different SMT systems in a Minimum Bayes Risk (MBR) manner. Each SMT system includes different tech- nology: syntactic pre-ordering, forest-to-string translation, and using external resources for domain adaptation and tar- get language modeling.
['Katsuhito Sudoh', 'Kevin Duh', 'Hajime Tsukada', 'Masaaki Nagata', 'Xianchao Wu', 'Takuya Matsuzaki', "Jun'ichi Tsujii"]
NTT-UT Statistical Machine Translation in NTCIR-9 PatentMT