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        <title>Regularization of Distinct Strategies for Unsupervised Question Generation</title>
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        <namePart type="given">Junmo</namePart>
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    <name type="personal">
        <namePart type="given">Sung-Hyon</namePart>
        <namePart type="family">Myaeng</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA. One approach to UQA is to train a QA model with questions generated automatically. However, the generated questions are either too similar to a word sequence in the context or too drifted from the semantics of the context, thereby making it difficult to train a robust QA model. We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated. Our experiments demonstrate that we have achieved the goal of generating higher-quality questions for UQA across diverse QA datasets and tasks. We also show that this method can be useful for creating a QA model with few-shot learning.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.293</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Regularization of Distinct Strategies for Unsupervised Question Generation
%A Kang, Junmo
%A Hong, Giwon
%A Puerto San Roman, Haritz
%A Myaeng, Sung-Hyon
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kang-etal-2020-regularization
%X Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA. One approach to UQA is to train a QA model with questions generated automatically. However, the generated questions are either too similar to a word sequence in the context or too drifted from the semantics of the context, thereby making it difficult to train a robust QA model. We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated. Our experiments demonstrate that we have achieved the goal of generating higher-quality questions for UQA across diverse QA datasets and tasks. We also show that this method can be useful for creating a QA model with few-shot learning.
%R 10.18653/v1/2020.findings-emnlp.293
%U https://aclanthology.org/2020.findings-emnlp.293
%U https://doi.org/10.18653/v1/2020.findings-emnlp.293
%P 3266-3277

Markdown (Informal)

[Regularization of Distinct Strategies for Unsupervised Question Generation](https://aclanthology.org/2020.findings-emnlp.293) (Kang et al., Findings 2020)

• Regularization of Distinct Strategies for Unsupervised Question Generation (Kang et al., Findings 2020)

ACL

• Junmo Kang, Giwon Hong, Haritz Puerto San Roman, and Sung-Hyon Myaeng. 2020. Regularization of Distinct Strategies for Unsupervised Question Generation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3266–3277, Online. Association for Computational Linguistics.