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Using supertags as source language context in SMT

Haque, Rejwanul and Naskar, Sudip Kumar and Ma, Yanjun and Way, Andy (2009) Using supertags as source language context in SMT. In: EAMT 2009 - 13th Annual Conference of the European Association for Machine Translation, 13-15 May 2009, Barcelona, Spain.

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Recent research has shown that Phrase-Based Statistical Machine Translation (PB-SMT) systems can benefit from two enhancements: (i) using words and POS tags as context-informed features on the source side; and (ii) incorporating lexical syntactic descriptions in the form of supertags on the target side. In this work we present a novel PB-SMT model that combines these two aspects by using supertags as source language contextinformed features. These features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. In our experiments two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar. We use a memory-based classification framework that enables the estimation of these features while avoiding problems of sparseness. Despite the differences between these two approaches, the supertaggers give similar improvements. We evaluate the performance of our approach on an English-to-Chinese translation task using a state-of-the-art phrase-based SMT system, and report an improvement of 7.88% BLEU score in translation quality when adding supertags as context-informed features.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:supertags; source language; 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 07/CE/I1142, SFI 05/IN/1732
ID Code:15160
Deposited On:15 Feb 2010 11:41 by DORAS Administrator. Last Modified 27 Apr 2010 11:24

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