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F-structure transfer-based statistical machine translation

Graham, Yvette and van Genabith, Josef and Bryl, Anton (2009) F-structure transfer-based statistical machine translation. In: Lexical Functional Grammar 2009, 13-16 July 2009, Cambridge, UK.

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Abstract

In this paper, we describe a statistical deep syntactic transfer decoder that is trained fully automatically on parsed bilingual corpora. Deep syntactic transfer rules are induced automatically from the f-structures of a LFG parsed bitext corpus by automatically aligning local f-structures, and inducing all rules consistent with the node alignment. The transfer decoder outputs the n-best TL f-structures given a SL f-structure as input by applying large numbers of transfer rules and searching for the best output using a log-linear model to combine feature scores. The decoder includes a fully integrated dependency-based tri-gram language model. We include an experimental evaluation of the decoder using different parsing disambiguation resources for the German data to provide a comparison of how the system performs with different German training and test parses.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:statistical machine translation;
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Initiatives and Centres > National Centre for Language Technology (NCLT)
Published in:Proceedings of the LFG09 Conference. . CSLI Publications.
Publisher:CSLI Publications
Official URL:http://cslipublications.stanford.edu/LFG/14/index.shtml
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, SFI P07077-6010
ID Code:15170
Deposited On:15 Feb 2010 13:44 by DORAS Administrator. Last Modified 27 Apr 2010 11:19

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