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Adjusting Machine Translation Datasets for Document-Level Cross-Language Information Retrieval: Methodology

  • Gennady Shtekh
  • Polina Kazakova
  • Nikita Nikitinsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11107)

Abstract

Evaluating the performance of Cross-Language Information Retrieval models is a rather difficult task since collecting and assessing substantial amount of data for CLIR systems evaluation could be a non-trivial and expensive process. At the same time, substantial number of machine translation datasets are available now. In the present paper we attempt to solve the problem stated above by suggesting a strict workflow for transforming machine translation datasets to a CLIR evaluation dataset (with automatically obtained relevance assessments), as well as a workflow for extracting a representative subsample from the initial large corpus of documents so that it is appropriate for further manual assessment. We also hypothesize and then prove by the number of experiments on the United Nations Parallel Corpus data that the quality of an information retrieval algorithm on the automatically assessed sample could be in fact treated as a reasonable metric.

Keywords

Cross-language information retrieval Document-level information retrieval CLIR evaluation CLIR datasets Parallel corpora Information retrieval methodology 

Notes

Acknowledgements

We would like to acknowledge the hard work and commitment from Ivan Menshikh throughout this study. We are also thankful to Anna Potapenko for offering very useful comments on the present paper, and Konstantin Vorontsov for encouragement and support.

The present research was supported by the Ministry of Education and Science of the Russian Federation under the unique research id RFMEFI57917X0143.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gennady Shtekh
    • 2
  • Polina Kazakova
    • 2
  • Nikita Nikitinsky
    • 1
  1. 1.Integrated SystemsMoscowRussia
  2. 2.National University of Science and Technology MISISMoscowRussia

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