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COLING
2008

Improving Statistical Machine Translation using Lexicalized Rule Selection

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Improving Statistical Machine Translation using Lexicalized Rule Selection
This paper proposes a novel lexicalized approach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) models which combine rich context information for selecting translation rules during decoding. We successfully integrate the MaxEnt-based rule selection models into the state-of-the-art syntax-based SMT model. Experiments show that our lexicalized approach for rule selection achieves statistically significant improvements over the state-of-the-art SMT system.
Zhongjun He, Qun Liu, Shouxun Lin
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where COLING
Authors Zhongjun He, Qun Liu, Shouxun Lin
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