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

  • Shadi SalehEmail author
  • Pavel Pecina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Machine Translation Mean Average Precision Good Translation Query Translation Retrieval Quality 
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.

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Formal and Applied Linguistics, Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic

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