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An augmented three-pass system combination framework: DCU combination system for WMT 2010

Way, Andy orcid logoORCID: 0000-0001-5736-5930, Pecina, Pavel and Du, Jinhua orcid logoORCID: 0000-0002-3267-4881 (2010) An augmented three-pass system combination framework: DCU combination system for WMT 2010. In: WMT 2010 - The Joint Fifth Workshop on Statistical Machine Translation and Metrics MATR, ACL 2010, 15-16 July 2010, Uppsala, Sweden.

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Abstract
This paper describes the augmented threepass system combination framework of the Dublin City University (DCU) MT group for the WMT 2010 system combination task. The basic three-pass framework includes building individual confusion networks (CNs), a super network, and a modified Minimum Bayes-risk (mCon- MBR) decoder. The augmented parts for WMT2010 tasks include 1) a rescoring component which is used to re-rank the N-best lists generated from the individual CNs and the super network, 2) a new hypothesis alignment metric – TERp – that is used to carry out English-targeted hypothesis alignment, and 3) more different backbone-based CNs which are employed to increase the diversity of the mConMBR decoding phase. We took part in the combination tasks of Englishto- Czech and French-to-English. Experimental results show that our proposed combination framework achieved 2.17 absolute points (13.36 relative points) and 1.52 absolute points (5.37 relative points) in terms of BLEU score on English-to- Czech and French-to-English tasks respectively than the best single system. We also achieved better performance on human evaluation.
Metadata
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 > Centre for Next Generation Localisation (CNGL)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://www.aclweb.org/anthology/W/W10/W10-1742.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, European Framework Programme 7
ID Code:15795
Deposited On:10 Nov 2010 14:02 by Shane Harper . Last Modified 09 Nov 2018 15:32
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