Three heads are better than one
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Learning to select a good translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Combination of Arabic preprocessing schemes for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Classifier combination techniques applied to coreference resolution
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Further meta-evaluation of machine translation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Machine translation system combination by confusion forest
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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We describe a new approach for synthetically combining the output of several different Machine Translation (MT) engines operating on the same input. The goal is to produce a synthetic combination that surpasses all of the original systems in translation quality. Our approach uses the individual MT engines as "black boxes" and does not require any explicit cooperation from the original MT systems. A decoding algorithm uses explicit word matches, in conjunction with confidence estimates for the various engines and a trigram language model in order to score and rank a collection of sentence hypotheses that are synthetic combinations of words from the various original engines. The highest scoring sentence hypothesis is selected as the final output of our system. Experiments, using several Arabic-to-English systems of similar quality, show a substantial improvement in the quality of the translation output.