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Feature Weight Optimization for Discourse-Level SMT
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology. (Datorlingvistik)
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
2013 (English)In: Proceedings of the Workshop on Discourse in Machine Translation (DiscoMT), Association for Computational Linguistics, 2013, 60-69 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present an approach to feature weight optimization for document-level decoding. This is an essential task for enabling future development of discourse-level statistical machine translation, as it allows easy integration of discourse features in the decoding process. We extend the framework of sentence-level feature weight optimization to the document-level. We show experimentally that we can get competitive and relatively stable results when using a standard set of features, and that this framework also allows us to optimize document- level features, which can be used to model discourse phenomena.

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2013. 60-69 p.
Keyword [en]
SMT, Cross-sentence SMT, Feature weight optimisation
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:uu:diva-207764OAI: oai:DiVA.org:uu-207764DiVA: diva2:649446
Conference
DiscoMT (Discourse in Machine Translation) 2013; 9 August 2013; Sofia, Bulgaria
Available from: 2013-09-18 Created: 2013-09-18 Last updated: 2013-09-18Bibliographically approved

Open Access in DiVA

DiscoMT2013(176 kB)155 downloads
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File name FULLTEXT01.pdfFile size 176 kBChecksum SHA-512
ebd03d181a83e4226eb1e52fb479bde3d230cc76a607f9181b66711c83e26be3f4f195313d32e56ec2aed622b8452f84080bac8a3ed1f1eb4e48ff928308d4f2
Type fulltextMimetype application/pdf

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http://www.aclweb.org/anthology/W13-3308

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Stymne, SaraHardmeier, ChristianTiedemann, JörgNivre, Joakim

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
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Output format
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