Cross-Lingual Question Answering Using Off-the-Shelf Machine Translation

  • Kisuh Ahn
  • Beatrice Alex
  • Johan Bos
  • Tiphaine Dalmas
  • Jochen L. Leidner
  • Matthew B. Smillie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3491)

Abstract

We show how to adapt an existing monolingual open-domain QA system to perform in a cross-lingual environment, using off-the-shelf machine translation software. In our experiments we use French and German as source language, and English as target language. For answering factoid questions, our system performs with an accuracy of 16% (German to English) and 20% (French to English), respectively. The loss of correctly answered questions caused by the MT component is estimated at 10% for French, and 15% for German. The accuracy of our system on correctly translated questions is 28% for German and 29% for French.

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References

  1. 1.
    Leidner, J.L., Bos, J., Dalmas, T., Curran, J.R., Clark, S., Bannard, C.J., Steedman, M., Webber, B.: The QED open-domain answer retrieval system for TREC 2003. In: Proceedings of the Twelfth Text Retrieval Conference (TREC 2003), pp. 595–599. NIST Special Publication 500-255, Gaithersburg (2004)Google Scholar
  2. 2.
    Voorhees, E.M.: Overview of TREC 2003. In: Proceedings of the Twelfth Text Retrieval Conference (TREC 2003), pp. 1–13. NIST Special Publication 500-255, Gaithersburg (2004)Google Scholar
  3. 3.
    Magnini, B., Romagnoli, S., Vallin, A., Herrera, J., Peñas, A., Peinado, V., Verdejo, F., de Rijke, M.: Creating the DISEQuA corpus: a test set for multilingual question answering. In: Peters, C., Gonzalo, J., Braschler, M., Kluck, M. (eds.) CLEF 2003. LNCS, vol. 3237, pp. 487–500. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Curran, J.R., Clark, S.: Investigating GIS and smoothing for maximum entropy taggers. In: Proceedings of the 11th Annual Meeting of the European Chapter of the Association for Computational Linguistics (EACL 2003), Budapest, Hungary, pp. 91–98 (2003)Google Scholar
  5. 5.
    Curran, J.R., Clark, S.: Language independent NER using a maximum entropy tagger. In: Proceedings of the Seventh Conference on Natural Language Learning (CoNLL 2003), Edmonton, Canada, pp. 164–167 (2003)Google Scholar
  6. 6.
    Briscoe, T., Carroll, J.: Robust accurate statistical annotation of general text. In: Proceedings of the 3rd International Conference on Language Resources and Evaluation, Las Palmas, Gran Canaria, pp. 1499–1504 (2002)Google Scholar
  7. 7.
    Kamp, H., Reyle, U.: From Discourse to Logic; An Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and DRT. Kluwer, Dordrecht (1993)Google Scholar
  8. 8.
    Fellbaum, C. (ed.): WordNet. An Electronic Lexical Database. The MIT Press, Cambridge (1998)MATHGoogle Scholar
  9. 9.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. Technical Report RC22176 (W0109-022), IBM Thomas J. Watson Research Center (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kisuh Ahn
    • 1
  • Beatrice Alex
    • 1
  • Johan Bos
    • 1
  • Tiphaine Dalmas
    • 1
  • Jochen L. Leidner
    • 1
  • Matthew B. Smillie
    • 1
  1. 1.University of EdinburghScotland, UK

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