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        <title>Domain-agnostic Question-Answering with Adversarial Training</title>
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    <name type="personal">
        <namePart type="given">Seanie</namePart>
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        <namePart type="given">Donggyu</namePart>
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            <title>Proceedings of the 2nd Workshop on Machine Reading for Question Answering</title>
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        <name type="personal">
            <namePart type="given">Adam</namePart>
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            <namePart type="given">Alon</namePart>
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            <namePart type="given">Minjoon</namePart>
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        <name type="personal">
            <namePart type="given">Danqi</namePart>
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    <abstract>Adapting models to new domain without finetuning is a challenging problem in deep learning. In this paper, we utilize an adversarial training framework for domain generalization in Question Answering (QA) task. Our model consists of a conventional QA model and a discriminator. The training is performed in the adversarial manner, where the two models constantly compete, so that QA model can learn domain-invariant features. We apply this approach in MRQA Shared Task 2019 and show better performance compared to the baseline model.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5826</identifier>
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%0 Conference Proceedings
%T Domain-agnostic Question-Answering with Adversarial Training
%A Lee, Seanie
%A Kim, Donggyu
%A Park, Jangwon
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lee-etal-2019-domain
%X Adapting models to new domain without finetuning is a challenging problem in deep learning. In this paper, we utilize an adversarial training framework for domain generalization in Question Answering (QA) task. Our model consists of a conventional QA model and a discriminator. The training is performed in the adversarial manner, where the two models constantly compete, so that QA model can learn domain-invariant features. We apply this approach in MRQA Shared Task 2019 and show better performance compared to the baseline model.
%R 10.18653/v1/D19-5826
%U https://aclanthology.org/D19-5826
%U https://doi.org/10.18653/v1/D19-5826
%P 196-202

Markdown (Informal)

[Domain-agnostic Question-Answering with Adversarial Training](https://aclanthology.org/D19-5826) (Lee et al., 2019)

• Domain-agnostic Question-Answering with Adversarial Training (Lee et al., 2019)

ACL

• Seanie Lee, Donggyu Kim, and Jangwon Park. 2019. Domain-agnostic Question-Answering with Adversarial Training. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 196–202, Hong Kong, China. Association for Computational Linguistics.