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2012

Learning Translation Consensus with Structured Label Propagation

7 years 1 months ago
Learning Translation Consensus with Structured Label Propagation
In this paper, we address the issue for learning better translation consensus in machine translation (MT) research, and explore the search of translation consensus from similar, rather than the same, source sentences or their spans. Unlike previous work on this topic, we formulate the problem as structured labeling over a much smaller graph, and we propose a novel structured label propagation for the task. We convert such graph-based translation consensus from similar source strings into useful features both for n-best output reranking and for decoding algorithm. Experimental results show that, our method can significantly improve machine translation performance on both IWSLT and NIST data, compared with a state-ofthe-art baseline.
Shujie Liu, Chi-Ho Li, Mu Li, Ming Zhou
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ACL
Authors Shujie Liu, Chi-Ho Li, Mu Li, Ming Zhou
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