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HMM word-to-phrase alignment with dependency constraints

Ma, Yanjun and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2010) HMM word-to-phrase alignment with dependency constraints. In: SSST 2010 - 4th Workshop on Syntax and Structure in Statistical Translation, 28 August 2010, Beijing, China.

In this paper, we extend the HMMwordto-phrase alignment model with syntactic dependency constraints. The syntactic dependencies between multiple words in one language are introduced into the model in a bid to produce coherent alignments. Our experimental results on a variety of Chinese–English data show that our syntactically constrained model can lead to as much as a 3.24% relative improvement in BLEU score over current HMM word-to-phrase alignment models on a Phrase-Based Statistical Machine Translation system when the training data is small, and a comparable performance compared to IBM model 4 on a Hiero-style system with larger training data. An intrinsic alignment quality evaluation shows that our alignment model with dependency constraints leads to improvements in both precision (by 1.74% relative) and recall (by 1.75% relative) over the model without dependency information.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Institutes and Centres > Centre for Next Generation Localisation (CNGL)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the 4th Workshop on Syntax and Structure in Statistical Translation. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://www.aclweb.org/anthology/W/W10/W10-3813.pdf
Copyright Information:© 2010 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
ID Code:15809
Deposited On:10 Nov 2010 16:19 by Shane Harper . Last Modified 09 Nov 2018 15:31

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