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A Novel Reordering Model for Statistical Machine Translation
University of Tehran.
University of Tehran.
Amirkabir University of Technology.
Linköping University, Department of Computer and Information Science, Human-Centered systems. (CILTLAB)ORCID iD: 0000-0003-1942-6063
2013 (English)In: Reserach in Computing Science: Advances in Natural Language Processing and Intelligent Learning Environments / [ed] Grigori Sidorov (Mexico), Gerhard Ritter ( USA ), Jean Serra (France ), Ulises Cortés ( Spain ), Mexico, 2013, 51-64 p.Conference paper (Refereed)
Abstract [en]

Word reordering is one of the fundamental problems of machine translation, and an important factor of its quality and efficiency. In this paper, we introduce a novel reordering model based on an innovative structure, named, phrasal dependency tree including syntactical and statistical information in context of a log-linear model. The phrasal dependency tree is a new modern syntactic structure based on dependency relations between contiguous non-syntactic phrases. In comparison with well-known and popular reordering models such as the distortion, lexicalized and hierarchical models, the experimental study demonstrates the superiority of our model regarding to the different evaluation measures. We evaluated the proposed model on a PersianEnglish SMT system. On average our model retrieved a significant impact on precision with comparable recall value respect to the lexicalized and distortion models, and is found to be effective for medium and long-distance reordering.

Place, publisher, year, edition, pages
Mexico, 2013. 51-64 p.
, Research in Computing Science, ISSN 1870-4069 ; 65
Keyword [en]
Reordering, phrase-based SMT, syntactical reordering model, long distance reordering
National Category
Computer Science
URN: urn:nbn:se:liu:diva-107634OAI: diva2:726149
13th Mexican International Conference on ARTIFICIAL INTELLIGENCE
Available from: 2014-06-17 Created: 2014-06-17 Last updated: 2014-06-17

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Maleki, Jalal
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