Title: An Overview of Tree-to-String Translation Models
Recent research on statistical machine translation has lead to the rapid development of syntax-based translation models, in which syntactic information can be exploited to direct translation. In this talk, I will give an overview of tree-to-string translation models, one of the state-of-the-art syntax-based models. In a tree-to-string model, the source side is a phrase structure parse tree and the target side is a string. This talk includes the following topics: (1) naive tree-to-string model, (2) tree-sequence based tree-to-string model, (3) context-aware tree-to-string model, and (4) forest-based tree-to-string model. Experimental results show that forest-based tree-to-string model outperforms hierarchical phrase-based model significantly.
Yang Liu is an Assistant Researcher at Institute of Computing Technology, Chinese Academy of Sciences. He graduated in Computer Science from Wuhan University in 2002. He received his PhD degree in Computer Science from Institute of Computing Technology, Chinese Academy of Sciences. His major research interests include statistical machine translation and Chinese information processing. His publications on discriminative word alignment and tree-to-string models have received wide attention. He served as PC member/Reviewer for TALIP, ACL, EMNLP, AMTA, and SSST.