An Augmented Three-Pass System Combination Framework: DCU Combination System for WMT 2010
Jinhua Du, Pavel Pecina, Andy Way · Workshop on Statistical Machine Translation · 2010
This paper describes the augmented three-pass system combination framework of the Dublin City University (DCU) MT group for the WMT 2010 system combination task. The basic three-pass framework includes building individual confusion networks (CNs), a super network, and a modified Minimum Bayes-risk (mConMBR) decoder. The augmented parts for WMT2010 tasks include 1) a rescoring component which is used to re-rank the N-best lists generated from the individual CNs and the super network, 2) a new hypothesis alignment metric -- TERp -- that is used to carry out English-targeted hypothesis alignment, and 3) more different backbone-based CNs which are employed to increase the diversity of the mConMBR decoding phase. We took part in the combination tasks of English-to-Czech and French-to-English. Experimental results show that our proposed combination framework achieved 2.17 absolute points (13.36 relative points) and 1.52 absolute points (5.37 relative points) in terms of BLEU score on English-to-Czech and French-to-English tasks respectively than the best single system. We also achieved better performance on human evaluation.