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EMNLP
2008

Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation

10 years 2 months ago
Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation
This paper proposes a novel maximum entropy based rule selection (MERS) model for syntax-based statistical machine translation (SMT). The MERS model combines local contextual information around rules and information of sub-trees covered by variables in rules. Therefore, our model allows the decoder to perform context-dependent rule selection during decoding. We incorporate the MERS model into a state-of-the-art linguistically syntax-based SMT model, the treeto-string alignment template model. Experiments show that our approach achieves significant improvements over the baseline system.
Qun Liu, Zhongjun He, Yang Liu, Shouxun Lin
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where EMNLP
Authors Qun Liu, Zhongjun He, Yang Liu, Shouxun Lin
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