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Enhancing Cross-Language Question Answering by Combining Multiple Question Translations

  • Rita Marina Aceves-Pérez
  • Manuel Montes-y-Gómez
  • Luis Villaseñor-Pineda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4394)

Abstract

One major problem of state-of-the-art Cross Language Question Answering systems is the translation of user questions. This paper proposes combining the potential of multiple translation machines in order to improve the final answering precision. In particular, it presents three different methods for this purpose. The first one focuses on selecting the most fluent translation from a given set; the second one combines the passages recovered by several question translations; finally, the third one constructs a new question reformulation by merging word sequences from different translations. Experimental results demonstrated that the proposed approaches allow reducing the error rates in relation to a monolingual question answering exercise.

Keywords

Language Model Translation Machine Target Language Question Answering Word Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rita Marina Aceves-Pérez
    • 1
  • Manuel Montes-y-Gómez
    • 1
  • Luis Villaseñor-Pineda
    • 1
  1. 1.Laboratorio de Tecnologías del Lenguaje, Instituto Nacional de Astrofísica, Óptica y ElectrónicaMéxico

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