Syntactic Skeleton-Based Translation

In this paper we propose an approach to modeling syntactically-motivated skeletal structure of source sentence for machine translation. This model allows for application of high-level syntactic transfer rules and low-level non-syntactic rules. It thus involves fully syntactic, non-syntactic, and partially syntactic derivations via a single grammar and decoding paradigm. On large-scale Chinese-English and English- Chinese translation tasks, we obtain an average improvement of +0. 9 BLEU across the newswire and web genres.

Authors

• Tong Xiao School of Computer Science and Engineering, Northeastern University, Shenyang, China NiuTrans Research, Shenyang, China
• Jingbo Zhu School of Computer Science and Engineering, Northeastern University, Shenyang, China NiuTrans Research, Shenyang, China
• Chunliang Zhang School of Computer Science and Engineering, Northeastern University, Shenyang, China
• Tongran Liu CAS Key Laboratory of Behavioral Science, Institute of Psychology, CAS, Beijing, China

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