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Modelling Pronominal Anaphora in Statistical Machine Translation
Fondazione Bruno Kessler, Human Language Technologies, Trento, Italy. (Datorlingvistik)
Fondazione Bruno Kessler, Trento. (Human Language Technologies Unit)
2010 (English)In: Proceedings of the 7th International Workshop on Spoken Language Translation, Paris. / [ed] Marcello Federico et al., Paris, 2010, 283-289 p.Conference paper, Published paper (Refereed)
Abstract [en]

Current Statistical Machine Translation (SMT) systems translate texts sentence by sentence without considering any cross-sentential context. Assuming independence between sentences makes it difficult to take certain translation decisions when the necessary information cannot be determined locally. We argue for the necessity to include cross-sentence dependencies in SMT. As a case in point, we study the problem of pronominal anaphora translation by manually evaluating German-English SMT output. We then present a word dependency model for SMT, which can represent links between word pairs in the same or in different sentences. We use this model to integrate the output of a coreference resol- ution system into English-German SMT with a view to improving the translation of anaphoric pronouns.

Place, publisher, year, edition, pages
Paris, 2010. 283-289 p.
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:uu:diva-154490OAI: oai:DiVA.org:uu-154490DiVA: diva2:420761
Conference
IWSLT (International Workshop on Spoken Language Translation); Paris, France; December 2nd and 3rd, 2010.
Available from: 2011-06-07 Created: 2011-06-05 Last updated: 2013-09-18Bibliographically approved

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hardmeier-federico:iwslt2010(363 kB)690 downloads
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Hardmeier, Christian
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Total: 690 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf