Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach

Thumbnail Image

Publication or External Link





Kathryn Baker, Michael Bloodgood, Chris Callison-Burch, Bonnie J Dorr, Nathaniel W Filardo, Lori Levin, Scott Miller, and Christine Piatko. 2010. Semantically-informed syntactic machine translation: A tree-grafting approach. In Proceedings of the Ninth Conference of the Association for Machine Translation in the Americas (AMTA), Denver, Colorado, October.


We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality—and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.