Syntax-based reordering model for phrasal statistical machine translation

To deal with the long-distance reordering, a linguistically syntax-based reordering model was presented for phrasal statistical machine translation. In this model, the syntax structure was decomposed according to the phrase segmentation to avoid the inconsistence between phrase and syntax. The reord...

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Vydáno v:Tongxin Xuebao s. 7 - 14
Hlavní autoři: XUE Yong-zeng, LI Sheng, ZHAO Tie-jun, YANG Mu-yun
Médium: Journal Article
Jazyk:čínština
Vydáno: Editorial Department of Journal on Communications 01.01.2008
Témata:
artificial intelligence
phrase
statistical translation model
syntax-based reordering
ISSN:1000-436X
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Abstract To deal with the long-distance reordering, a linguistically syntax-based reordering model was presented for phrasal statistical machine translation. In this model, the syntax structure was decomposed according to the phrase segmentation to avoid the inconsistence between phrase and syntax. The reordering sequence of the sub-structures of a parse tree was decided by the word and phrase alignments. The reordering probability of the sub-structure was calculated on the reordering probabilities of the inside nodes, which was defined as a feature function of the log-linear statistical translation model. Experimental results show that the BLEU scores of the translation results were significantly improved compared with a conventional statistical phrase-based model. Therefore, it is effective to introduce the linguistic syntax for phrase reordering. The presented reordering model is able to efficiently incorporate the syntax into the translation process of phrases.
AbstractList To deal with the long-distance reordering, a linguistically syntax-based reordering model was presented for phrasal statistical machine translation. In this model, the syntax structure was decomposed according to the phrase segmentation to avoid the inconsistence between phrase and syntax. The reordering sequence of the sub-structures of a parse tree was decided by the word and phrase alignments. The reordering probability of the sub-structure was calculated on the reordering probabilities of the inside nodes, which was defined as a feature function of the log-linear statistical translation model. Experimental results show that the BLEU scores of the translation results were significantly improved compared with a conventional statistical phrase-based model. Therefore, it is effective to introduce the linguistic syntax for phrase reordering. The presented reordering model is able to efficiently incorporate the syntax into the translation process of phrases.
Author XUE Yong-zeng
LI Sheng
YANG Mu-yun
ZHAO Tie-jun
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Snippet To deal with the long-distance reordering, a linguistically syntax-based reordering model was presented for phrasal statistical machine translation. In this...
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SubjectTerms artificial intelligence
phrase
statistical translation model
syntax-based reordering
Title Syntax-based reordering model for phrasal statistical machine translation
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