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Tuning syntactically enhanced word alignment for statistical machine translation

Ma, Yanjun and Lambert, Patrik and Way, Andy (2009) Tuning syntactically enhanced word alignment for statistical machine translation. In: EAMT 2009 - 13th Annual Conference of the European Association for Machine Translation, 13-15 May 2009, Barcelona, Spain.

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We introduce a syntactically enhanced word alignment model that is more flexible than state-of-the-art generative word alignment models and can be tuned according to different end tasks. First of all, this model takes the advantages of both unsupervised and supervised word alignment approaches by obtaining anchor alignments from unsupervised generative models and seeding the anchor alignments into a supervised discriminative model. Second, this model offers the flexibility of tuning the alignment according to different optimisation criteria. Our experiments show that using our word alignment in a Phrase-Based Statistical Machine Translation system yields a 5.38% relative increase on IWSLT 2007 task in terms of BLEU score.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:phrase-based statistical machine translation;
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
Published in:Proceedings of the 13th Annual Conference of the EAMT. . European Association for Machine Translation.
Publisher:European Association for Machine Translation
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 O5/IN/1732, SFI 07/CE/I1142
ID Code:15158
Deposited On:15 Feb 2010 11:29 by DORAS Administrator. Last Modified 27 Apr 2010 11:42

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