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

Kathryn Baker, Michael Bloodgood, Chris Callison-Burch, Bonnie Dorr, Nathaniel Filardo, Lori Levin, Scott Miller, Christine Piatko


Abstract
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.
Anthology ID:
2010.amta-papers.7
Volume:
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 31-November 4
Year:
2010
Address:
Denver, Colorado, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
Language:
URL:
https://aclanthology.org/2010.amta-papers.7
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2010.amta-papers.7.pdf