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Boosting English-Chinese Machine Transliteration via High Quality Alignment and Multilingual Resources
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.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
2015 (English)In: Proceedings of the Fifth Named Entity Workshop, Association for Computational Linguistics , 2015, 56-60 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents our machine transliteration systems developed for the NEWS 2015 machine transliteration shared task. Our systems are applied to two tasks: English to Chinese and Chinese to English. For standard runs, in which only official data sets are used, we build phrase-based transliteration models with refined alignments provided by the M2M-aligner. For non-standard runs, we add multilingual resources to the systems designed for the standard runs and build different language specific transliteration systems. Linear regression is adopted to rerank the outputs afterwards, which significantly improves the overall transliteration performance.

Place, publisher, year, edition, pages
Association for Computational Linguistics , 2015. 56-60 p.
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:uu:diva-268921OAI: oai:DiVA.org:uu-268921DiVA: diva2:881662
Conference
Fifth Named Entity Workshop, joint with 53rd ACL and the 7th IJCNLP, July 31 2015, Beijing, China
Available from: 2015-12-11 Created: 2015-12-11 Last updated: 2016-02-19Bibliographically approved

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Yan, ShaoTiedemann, JörgNivre, Joakim

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  • nn-NO
  • nn-NB
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