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Towards End-to-End Multilingual Question Answering

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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. https://github.com/facebookresearch/MUSE

  2. https://github.com/shuzi/insuranceQA

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

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

  5. https://github.com/edloginova/neural_mlqa

  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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  1. University of Ghent, Tweekerkenstraat 2, 9000, Ghent, Belgium

    Ekaterina Loginova

  2. DFKI, Saarbrücken, Germany

    Stalin Varanasi & Günter Neumann

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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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