emnlp emnlp2012 emnlp2012-95 emnlp2012-95-reference knowledge-graph by maker-knowledge-mining
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Author: Zhengxian Gong ; Min Zhang ; Chew Lim Tan ; Guodong Zhou
Abstract: Tense is a small element to a sentence, however, error tense can raise odd grammars and result in misunderstanding. Recently, tense has drawn attention in many natural language processing applications. However, most of current Statistical Machine Translation (SMT) systems mainly depend on translation model and language model. They never consider and make full use of tense information. In this paper, we propose n-gram-based tense models for SMT and successfully integrate them into a state-of-the-art phrase-based SMT system via two additional features. Experimental results on the NIST Chinese-English translation task show that our proposed tense models are very effective, contributing performance improvement by 0.62 BLUE points over a strong baseline. 1
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