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Marker-based filtering of bilingual phrase pairs for SMT

Sánchez-Martínez, Felipe and Way, Andy (2009) Marker-based filtering of bilingual phrase pairs for SMT. In: EAMT 2009 - 13th Annual Conference of the European Association for Machine Translation, 13-15 May 2009, Barcelona, Spain.

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Abstract

State-of-the-art statistical machine translation systems make use of a large translation table obtained after scoring a set of bilingual phrase pairs automatically extracted from a parallel corpus. The number of bilingual phrase pairs extracted from a pair of aligned sentences grows exponentially as the length of the sentences increases; therefore, the number of entries in the phrase table used to carry out the translation may become unmanageable, especially when online, 'on demand' translation is required in real time. We describe the use of closed-class words to filter the set of bilingual phrase pairs extracted from the parallel corpus by taking into account the alignment information and the type of the words involved in the alignments. On four European language pairs, we show that our simple yet novel approach can filter the phrase table by up to a third yet still provide competitive results compared to the baseline. Furthermore, it provides a nice balance between the unfiltered approach and pruning using stop words, where the deterioration in translation quality is unacceptably high.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:statistical machine translation;
Subjects:Computer Science > Machine translating
DCU Faculties and Centres: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:http://www.talp.cat/eamt09/index.php/programme
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, SFI 05/IN/1732, SFI 06/RF/CMS064
ID Code:15156
Deposited On:15 Feb 2010 11:17 by DORAS Administrator. Last Modified 27 Apr 2010 11:55

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