Syntax Augmented Inversion Transduction Grammars for Machine Translation

摘要

In this paper we propose a novel method for inferring an Inversion Transduction Grammar (ITG) from a bilingual parallel corpus with linguistic information from the source or target language. Our method combines bilingual ITG parse trees with monolingual linguistic trees in order to obtain a Syntax Augmented ITG (SAITG). The use of a modified bilingual parsing algorithm with bracketing information makes possible that each bilingual subtree has a correspondent subtree in the monolingual parsing. In addition, several binarization techniques have been tested for the resulting SAITG. In order to evaluate the effects of the use of SAITGs in Machine Translation tasks, we have used them in an ITG-based machine translation decoder. The results obtained using SAITGs with the decoder for the IWSLT-08 Chinese-English machine translation task produce significant improvements in BLEU.