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

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dc.contributor.authorBaker, Kathryn
dc.contributor.authorBloodgood, Michael
dc.contributor.authorCallison-Burch, Chris
dc.contributor.authorDorr, Bonnie
dc.contributor.authorFilardo, Nathaniel
dc.contributor.authorLevin, Lori
dc.contributor.authorMiller, Scott
dc.contributor.authorPiatko, Christine
dc.date.accessioned2014-08-23T01:48:42Z
dc.date.available2014-08-23T01:48:42Z
dc.date.issued2010-10
dc.description.abstractWe 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.en_US
dc.description.sponsorshipWe thank Basis Technology Corporation for their generous contribution of software components to this work. This work is supported, in part, by the Johns Hopkins Human Language Technology Center of Excellence, by the National Science Foundation under grant IIS-0713448, and by BBN Technologies under GALE DARPA/IPTO Contract No. HR0011-06-C-0022. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor.en_US
dc.identifierhttps://doi.org/10.13016/M2H59F
dc.identifier.citationKathryn 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.en_US
dc.identifier.urihttp://hdl.handle.net/1903/15579
dc.language.isoen_USen_US
dc.relation.isAvailableAtCenter for Advanced Study of Language
dc.relation.isAvailableAtDigitial Repository at the University of Maryland
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md)
dc.subjectcomputer scienceen_US
dc.subjectartificial intelligenceen_US
dc.subjectstatistical methodsen_US
dc.subjectmachine learningen_US
dc.subjectcomputational linguisticsen_US
dc.subjectnatural language processingen_US
dc.subjecthuman language technologyen_US
dc.subjecttranslation technologyen_US
dc.subjectmachine translationen_US
dc.subjectstatistical machine translationen_US
dc.subjectsemantically-informed machine translationen_US
dc.subjectsemantically-informed syntactic machine translationen_US
dc.subjectmodalityen_US
dc.subjectnegationen_US
dc.subjectnamed entitiesen_US
dc.subjecttree-graftingen_US
dc.subjectUrdu-English translationen_US
dc.titleSemantically-Informed Syntactic Machine Translation: A Tree-Grafting Approachen_US
dc.typeArticleen_US

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