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Improving word alignment using syntactic dependencies

Ma, Yanjun and Ozdowska, Sylwia and Sun, Yanli and Way, Andy (2008) Improving word alignment using syntactic dependencies. In: ACL08-SSST - Proceedings of ACL08 workshop on Syntax and Structure in Statistical Translation, 20 June 2008, Columbus, Ohio, USA..

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Abstract

We introduce a word alignment framework that facilitates the incorporation of syntax encoded in bilingual dependency tree pairs. Our model consists of two sub-models: an anchor word alignmentmodel which aims to find a set of high-precision anchor links and a syntax enhanced word alignment model which focuses on aligning the remaining words relying on dependency information invoked by the acquired anchor links. We show that our syntax enhanced word alignment approach leads to a 10.32% and 5.57% relative decrease in alignment error rate compared to a generative word alignment model and a syntax-proof discriminative word alignment model respectively. Furthermore, our approach is evaluated extrinsically using a phrase-based statistical machine translation system. The results show that SMT systems based on our word alignment approach tend to generate shorter outputs. Without length penalty, using our word alignments yields statistically significant improvement in Chinese–English machine translation in comparison with the baseline word alignment.

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Initiatives and Centres > National Centre for Language Technology (NCLT)
DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Applied Language and Intercultural Studies
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Association for Computational Linguistics
Official URL:http://www.cse.ust.hk/~dekai/ssst/
Copyright Information:© 2008 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, SFI OS/IN/1732
ID Code:560
Deposited On:15 Sep 2008 12:31 by DORAS Administrator. Last Modified 27 Apr 2010 12:35

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