A Tree-to-String Phrase-based Model for Statistical Machine Translation
نویسندگان
چکیده
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 phrasebased 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 sourceparsed 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.
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تاریخ انتشار 2008