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• W2117164663 abstract "This paper presents collaborative decoding (co-decoding), a new method to improve machine translation accuracy by leveraging translation consensus between multiple machine translation decoders. Different from system combination and MBR decoding, which post-process the n-best lists or word lattice of machine translation decoders, in our method multiple machine translation decoders collaborate by exchanging partial translation results. Using an iterative decoding approach, n-gram agreement statistics between translations of multiple decoders are employed to re-rank both full and partial hypothesis explored in decoding. Experimental results on data sets for NIST Chinese-to-English machine translation task show that the co-decoding method can bring significant improvements to all baseline decoders, and the outputs from co-decoding can be used to further improve the result of system combination." @default.
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• W2117164663 date "2009-01-01" @default.
• W2117164663 modified "2023-09-24" @default.
• W2117164663 title "Collaborative decoding" @default.
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• W2117164663 doi "https://doi.org/10.3115/1690219.1690228" @default.
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