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2009

Collaborative Decoding: Partial Hypothesis Re-ranking Using Translation Consensus between Decoders

9 years 8 days ago
Collaborative Decoding: Partial Hypothesis Re-ranking Using Translation Consensus between Decoders
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 postprocess 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.
Mu Li, Nan Duan, Dongdong Zhang, Chi-Ho Li, Ming Z
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Mu Li, Nan Duan, Dongdong Zhang, Chi-Ho Li, Ming Zhou
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