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Reranking Hypotheses of Machine-Translated Queries for Cross-Lingual Information Retrieval

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Book cover Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9822))

Abstract

Machine Translation (MT) systems employed to translate queries for Cross-Lingual Information Retrieval typically produce a single translation with maximum translation quality. This, however, might not be optimal with respect to retrieval quality and other translation variants might lead to better retrieval results. In this paper, we explore a method using multiple translations produced by an MT system, which are reranked using a supervised machine-learning method trained to directly optimize retrieval quality. We experiment with various types of features and the results obtained on the medical-domain test collection from the CLEF eHealth Lab series show significant improvement of retrieval quality compared to a system using single translation provided by MT.

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Notes

  1. 1.

    http://translate.google.com/.

  2. 2.

    http://search.cpan.org/dist/HTML-Strip/Strip.pm.

  3. 3.

    http://www.ncbi.nlm.nih.gov/.

  4. 4.

    http://trec.nist.gov/trec_eval.

  5. 5.

    http://www.khresmoi.eu/.

  6. 6.

    https://www.r-project.org/.

  7. 7.

    https://www.bing.com/translator/.

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Acknowledgments

This research was supported by the Czech Science Foundation (grant no. P103/12/G084) and the EU H2020 project KConnect (contract no. 644753).

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Authors and Affiliations

  1. Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic

    Shadi Saleh & Pavel Pecina

Authors
  1. Shadi Saleh
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  2. Pavel Pecina
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Corresponding author

Correspondence to Shadi Saleh .

Editor information

Editors and Affiliations

  1. Universität Duisburg-Essen , Duisburg, Germany

    Norbert Fuhr

  2. Universidade de Évora , Évora, Portugal

    Paulo Quaresma

  3. University of Évora , Évora, Portugal

    Teresa Gonçalves

  4. Aalborg University Copenhagen , Copenhagen, Denmark

    Birger Larsen

  5. University of Stavanger , Stavanger, Norway

    Krisztian Balog

  6. University of Glasgow , Glasgow, United Kingdom

    Craig Macdonald

  7. University of Padua , Padua, Italy

    Linda Cappellato

  8. University of Padua , Padua, Italy

    Nicola Ferro

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Saleh, S., Pecina, P. (2016). Reranking Hypotheses of Machine-Translated Queries for Cross-Lingual Information Retrieval. In: Fuhr, N., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2016. Lecture Notes in Computer Science(), vol 9822. Springer, Cham. https://doi.org/10.1007/978-3-319-44564-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-44564-9_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-44564-9

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