Graham, Yvette, van Genabith, Josef ORCID: 0000-0003-1322-7944 and Bryl, Anton (2009) F-structure transfer-based statistical machine translation. In: Lexical Functional Grammar 2009, 13-16 July 2009, Cambridge, UK.
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.
Metadata
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 Institutes 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.... |
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 21 Jan 2022 16:31 |
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