Roy Tromble
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Learning linear ordering problems for better translation
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Jason Eisner
EMNLP '09: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Morristown, NJ, USA, pp. 1007-1016
Lattice Minimum Bayes-Risk Decoding for Statistical Machine Translation
Franz Och
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 620-629
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We present Minimum Bayes-Risk (MBR) decoding over translation lattices that compactly encode
a huge number of translation hypotheses. We describe conditions on the loss function
that will enable efficient implementation of MBR decoders on lattices. We introduce
an approximation to the BLEU score~\cite{papineni01} that satisfies these conditions. The MBR decoding under this approximate BLEU is realized using Weighted Finite State Automata. Our experiments show that the Lattice MBR decoder yields moderate, consistent gains in
translation performance over N-best MBR decoding on Arabic-to-English, Chinese-to-English and English-to-Chinese translation tasks. We conduct a range of experiments to
understand why Lattice MBR improves upon N-best MBR and also study the impact of various parameters on MBR performance.
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