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Source-side context-informed hypothesis alignment for combining outputs from machine translation systems

Du, Jinhua and Ma, Yanjun and Way, Andy (2009) Source-side context-informed hypothesis alignment for combining outputs from machine translation systems. In: MT Summit XII - The twelfth Machine Translation Summit, 26-30 August 2009, Ottawa, Canada.

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This paper presents a new hypothesis alignment method for combining outputs of multiple machine translation (MT) systems. Traditional hypothesis alignment algorithms such as TER, HMM and IHMM do not directly utilise the context information of the source side but rather address the alignment issues via the output data itself. In this paper, a source-side context-informed (SSCI) hypothesis alignment method is proposed to carry out the word alignment and word reordering issues. First of all, the source–target word alignment links are produced as the hidden variables by exporting source phrase spans during the translation decoding process. Secondly, a mapping strategy and normalisation model are employed to acquire the 1- to-1 alignment links and build the confusion network (CN). The source-side context-based method outperforms the state-of-the-art TERbased alignment model in our experiments on the WMT09 English-to-French and NIST Chinese-to-English data sets respectively. Experimental results demonstrate that our proposed approach scores consistently among the best results across different data and language pair conditions.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:machine translation systems;
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL)
Research Initiatives and Centres > National Centre for Language Technology (NCLT)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, SFI 07/CE/I1142
ID Code:15163
Deposited On:15 Feb 2010 12:11 by DORAS Administrator. Last Modified 27 Apr 2010 11:11

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