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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.

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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