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|Title:||N-gram-based tense models for statistical machine translation|
|Source:||Gong, Z.,Zhang, M.,Tan, C.,Zhou, G. (2012). N-gram-based tense models for statistical machine translation. EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference : 276-285. ScholarBank@NUS Repository.|
|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. © 2012 Association for Computational Linguistics.|
|Source Title:||EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference|
|Appears in Collections:||Staff Publications|
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