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

ACL ID P09-1066
Title Collaborative Decoding: Partial Hypothesis Re-ranking Using Translation Consensus between Decoders
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

This paper presents collaborative decoding (co-decoding), a new method to improve ma- chine translation accuracy by leveraging trans- lation consensus between multiple machine translation decoders. Different from system combination and MBR decoding, which post- process the n-best lists or word lattice of ma- chine translation decoders, in our method mul- tiple machine translation decoders collaborate by exchanging partial translation results. Us- ing 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 improvemen...