Towards End-to-End Multilingual Question Answering

Abstract

Multilingual question answering (MLQA) is a critical part of an accessible natural language interface. However, current solutions demonstrate performance far below that of monolingual systems. We believe that deep learning approaches are likely to improve performance in MLQA drastically. This work aims to discuss the current state-of-the-art and remaining challenges. We outline requirements and suggestions for practical parallel data collection and describe existing methods, benchmarks and datasets. We also demonstrate that a simple translation of texts can be inadequate in case of Arabic, English and German languages (on InsuranceQA and SemEval datasets), and thus more sophisticated models are required. We hope that our overview will re-ignite interest in multilingual question answering, especially with regard to neural approaches.

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Notes

  1. 1.

    https://github.com/facebookresearch/MUSE

  2. 2.

    https://github.com/shuzi/insuranceQA

  3. 3.

    http://alt.qcri.org/semeval2017/task3/

  4. 4.

    The parameters are as follows: skip-gram, window 5, negative-sampling rate − 1/1000.

  5. 5.

    https://github.com/edloginova/neural_mlqa

  6. 6.

    http://lucene.apache.org/solr/

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Acknowledgments

This work was partially supported by the German Federal Ministry of Education and Research (BMBF) through the project DEEPLEE (01IW17001).

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Correspondence to Ekaterina Loginova.

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Loginova, E., Varanasi, S. & Neumann, G. Towards End-to-End Multilingual Question Answering. Inf Syst Front 23, 227–241 (2021). https://doi.org/10.1007/s10796-020-09996-1

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Keywords

  • Question answering
  • Multilingual natural language processing
  • Neural natural language processing
  • Deep learning
  • Multilingual question answering
  • Cross-lingual question answering