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Rethinking Adversarial Training for Language Adaptation

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Text, Speech, and Dialogue (TSD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12848))

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Abstract

Recent advances in pre-trained language models revolutionized the field of natural language processing. However, these approaches require large-scale annotated resources, that are only available for some languages. Collecting data in every language is unrealistic, hence the growing interest in cross-lingual methods that can leverage the knowledge acquired in one language to different target languages. To address these challenges, Adversarial Training has been successfully employed in a variety of tasks and languages. Empirical analysis for the task of natural language inference suggests that, with the advent of neural language models, more challenging auxiliary tasks should be formulated to further improve the transfer of knowledge via Adversarial Training. We propose alternative formulations for the adversarial component, which we believe to be promising in different cross-lingual scenarios.

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Acknowledgments

Gil Rocha is supported by a PhD grant (SFRH/BD/140125/2018) from Fundação para a Ciência e a Tecnologia (FCT). This research is supported by LIACC (FCT/UID/CEC/0027/2020) and by project DARGMINTS, funded by FCT (POCI/01/0145/FEDER/031460).

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  1. Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC), Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Gil Rocha & Henrique Lopes Cardoso

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  1. Gil Rocha
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  2. Henrique Lopes Cardoso
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Correspondence to Gil Rocha .

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Editors and Affiliations

  1. University of West Bohemia, Pilsen, Czech Republic

    Kamil Ekštein

  2. University of West Bohemia, Pilsen, Czech Republic

    František Pártl

  3. University of West Bohemia, Pilsen, Czech Republic

    Miloslav Konopík

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Rocha, G., Lopes Cardoso, H. (2021). Rethinking Adversarial Training for Language Adaptation. In: Ekštein, K., Pártl, F., Konopík, M. (eds) Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science(), vol 12848. Springer, Cham. https://doi.org/10.1007/978-3-030-83527-9_21

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  • DOI: https://doi.org/10.1007/978-3-030-83527-9_21

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  • Print ISBN: 978-3-030-83526-2

  • Online ISBN: 978-3-030-83527-9

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