BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
MaTrEx: the DCU MT system for WMT 2009
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Fluency, adequacy, or HTER?: exploring different human judgments with a tunable MT metric
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
N-gram posterior probabilities for statistical machine translation
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
A Three-Pass System Combination Framework by Combining Multiple Hypothesis Alignment Methods
IALP '09 Proceedings of the 2009 International Conference on Asian Language Processing
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
MaTrEx: the DCU MT system for WMT 2010
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
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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.