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Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction
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 2013 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2013, 380-391 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the task of predicting the correct French translations of third-person subject pronouns in English discourse, a problem that is relevant as a prerequisite for machine translation and that requires anaphora resolution. We present an approach based on neural networks that models anaphoric links as latent variables and show that its performance is competitive with that of a system with separate anaphora resolution while not requiring any coreference-annotated training data. This demonstrates that the information contained in parallel bitexts can successfully be used to acquire knowledge about pronominal anaphora in an unsupervised way.

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2013. 380-391 p.
Keyword [en]
Coreference resolution, SMT, Cross-sentence SMT
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:uu:diva-213737OAI: oai:DiVA.org:uu-213737DiVA: diva2:683314
Conference
EMNLP 2013; Conference on Empirical Methods in Natural Language Processing; 18-21 October 2013; Seattle, WA, USA
Available from: 2014-01-03 Created: 2014-01-03 Last updated: 2014-01-09Bibliographically approved

Open Access in DiVA

EMNLP2013(285 kB)204 downloads
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File name FULLTEXT01.pdfFile size 285 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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http://www.aclweb.org/anthology/D13-1037Conference website

Authority records BETA

Hardmeier, ChristianTiedemann, JörgNivre, Joakim

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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