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- W2160133551 abstract "Though phrase-based SMT has achieved high translation quality, it still lacks of generalization ability to capture word order differences between languages. In this paper we describe a general method for tree-to-string phrase-based SMT. We study how syntactic transformation is incorporated into phrase-based SMT and its effectiveness. We design syntactic transformation models using unlexicalized form of synchronous context-free grammars. These models can be learned from source-parsed bitext. Our system can naturally make use of both constituent and non-constituent phrasal translations in the decoding phase. We considered various levels of syntactic analysis ranging from chunking to full parsing. Our experimental results of English-Japanese and English-Vietnamese translation showed a significant improvement over two baseline phrase-based SMT systems." @default.
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- W2160133551 date "2008-01-01" @default.
- W2160133551 modified "2023-09-23" @default.
- W2160133551 title "A tree-to-string phrase-based model for statistical machine translation" @default.
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- W2160133551 doi "https://doi.org/10.3115/1596324.1596349" @default.
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