A Substitution-Translation-Restoration Framework for Handling Unknown Words in Statistical Machine Translation

Unknown words are one of the key factors that greatly affect the translation quality. Traditionally, nearly all the related researches focus on obtaining the translation of the unknown words. However, these approaches have two disadvantages. On the one hand, they usually rely on many additional reso...

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Published in:	Journal of computer science and technology Vol. 28; no. 5; pp. 907 - 918
Main Authors:	Zhang, Jia-Jun, Zhai, Fei-Fei, Zong, Cheng-Qing
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.09.2013 Springer Nature B.V National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Subjects:	Artificial Intelligence Bilingualism Computer Science Computer simulation Data mining Data Structures and Information Theory Experiments Handling Information Systems Applications (incl.Internet) Language Machine translation Mathematical analysis Mathematical models Regular Paper Semantics Sentences SMT Software Engineering Studies Theory of Computation Translations Words (language) 框架统计机器翻译网络数据翻译质量翻译过程语义模型语言模型 Statistical machine translation Distributional semantics Bidirectional language model bidirectional language model distributional semantics statistical machine translation
ISSN:	1000-9000, 1860-4749
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Abstract	Unknown words are one of the key factors that greatly affect the translation quality. Traditionally, nearly all the related researches focus on obtaining the translation of the unknown words. However, these approaches have two disadvantages. On the one hand, they usually rely on many additional resources such as bilingual web data; on the other hand, they cannot guarantee good reordering and lexical selection of surrounding words. This paper gives a new perspective on handling unknown words in statistical machine translation （SMT）. Instead of making great efforts to find the translation of unknown words, we focus on determining the semantic function of the unknown word in the test sentence and keeping the semantic function unchanged in the translation process. In this way, unknown words can help the phrase reordering and lexical selection of their surrounding words even though they still remain untranslated. In order to determine the semantic function of an unknown word, we employ the distributional semantic model and the bidirectional language model. Extensive experiments on both phrase-based and linguistically syntax-based SMT models in Chinese-to-English translation show that our method can substantially improve the translation quality.
AbstractList	Unknown words are one of the key factors that greatly affect the translation quality. Traditionally, nearly all the related researches focus on obtaining the translation of the unknown words. However, these approaches have two disadvantages. On the one hand, they usually rely on many additional resources such as bilingual web data; on the other hand, they cannot guarantee good reordering and lexical selection of surrounding words. This paper gives a new perspective on handling unknown words in statistical machine translation (SMT). Instead of making great efforts to find the translation of unknown words, we focus on determining the semantic function of the unknown word in the test sentence and keeping the semantic function unchanged in the translation process. In this way, unknown words can help the phrase reordering and lexical selection of their surrounding words even though they still remain untranslated. In order to determine the semantic function of an unknown word, we employ the distributional semantic model and the bidirectional language model. Extensive experiments on both phrase-based and linguistically syntax-based SMT models in Chinese-to-English translation show that our method can substantially improve the translation quality.[PUBLICATION ABSTRACT] Unknown words are one of the key factors that greatly affect the translation quality. Traditionally, nearly all the related researches focus on obtaining the translation of the unknown words. However, these approaches have two disadvantages. On the one hand, they usually rely on many additional resources such as bilingual web data; on the other hand, they cannot guarantee good reordering and lexical selection of surrounding words. This paper gives a new perspective on handling unknown words in statistical machine translation (SMT). Instead of making great efforts to find the translation of unknown words, we focus on determining the semantic function of the unknown word in the test sentence and keeping the semantic function unchanged in the translation process. In this way, unknown words can help the phrase reordering and lexical selection of their surrounding words even though they still remain untranslated. In order to determine the semantic function of an unknown word, we employ the distributional semantic model and the bidirectional language model. Extensive experiments on both phrase-based and linguistically syntax-based SMT models in Chinese-to-English translation show that our method can substantially improve the translation quality. Unknown words are one of the key factors that greatly affect the translation quality. Traditionally, nearly all the related researches focus on obtaining the translation of the unknown words. However, these approaches have two disadvantages. On the one hand, they usually rely on many additional resources such as bilingual web data; on the other hand, they cannot guarantee good reordering and lexical selection of surrounding words. This paper gives a new perspective on handling unknown words in statistical machine translation （SMT）. Instead of making great efforts to find the translation of unknown words, we focus on determining the semantic function of the unknown word in the test sentence and keeping the semantic function unchanged in the translation process. In this way, unknown words can help the phrase reordering and lexical selection of their surrounding words even though they still remain untranslated. In order to determine the semantic function of an unknown word, we employ the distributional semantic model and the bidirectional language model. Extensive experiments on both phrase-based and linguistically syntax-based SMT models in Chinese-to-English translation show that our method can substantially improve the translation quality.
Author	Zong, Cheng-Qing Zhang, Jia-Jun Zhai, Fei-Fei
AuthorAffiliation	National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Notes	Unknown words are one of the key factors that greatly affect the translation quality. Traditionally, nearly all the related researches focus on obtaining the translation of the unknown words. However, these approaches have two disadvantages. On the one hand, they usually rely on many additional resources such as bilingual web data; on the other hand, they cannot guarantee good reordering and lexical selection of surrounding words. This paper gives a new perspective on handling unknown words in statistical machine translation （SMT）. Instead of making great efforts to find the translation of unknown words, we focus on determining the semantic function of the unknown word in the test sentence and keeping the semantic function unchanged in the translation process. In this way, unknown words can help the phrase reordering and lexical selection of their surrounding words even though they still remain untranslated. In order to determine the semantic function of an unknown word, we employ the distributional semantic model and the bidirectional language model. Extensive experiments on both phrase-based and linguistically syntax-based SMT models in Chinese-to-English translation show that our method can substantially improve the translation quality. 11-2296/TP statistical machine translation, distributional semantics, bidirectional language model Jia-Jun Zhang , Member, CCF, FeimFei Zhai and Cheng-Qing Zong , Senior Member, CCF （National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China E-marl： {jjzhang, ffzhai, cqzong}@nlpr.ia.ac.cn Received December 4, 2012; revised May 7, 2013） ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
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SubjectTerms	Artificial Intelligence Bilingualism Computer Science Computer simulation Data mining Data Structures and Information Theory Experiments Handling Information Systems Applications (incl.Internet) Language Machine translation Mathematical analysis Mathematical models Regular Paper Semantics Sentences SMT Software Engineering Studies Theory of Computation Translations Words (language) 框架统计机器翻译网络数据翻译质量翻译过程语义模型语言模型
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Title	A Substitution-Translation-Restoration Framework for Handling Unknown Words in Statistical Machine Translation
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