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        <title>Neural Question Generation using Interrogative Phrases</title>
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    <name type="personal">
        <namePart type="given">Yuichi</namePart>
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        <namePart type="given">Sho</namePart>
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    <name type="personal">
        <namePart type="given">Naoaki</namePart>
        <namePart type="family">Okazaki</namePart>
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        <dateIssued>2019-oct–nov</dateIssued>
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            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
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            <namePart type="given">Kees</namePart>
            <namePart type="family">van Deemter</namePart>
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        <name type="personal">
            <namePart type="given">Chenghua</namePart>
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    <abstract>Question Generation (QG) is the task of generating questions from a given passage. One of the key requirements of QG is to generate a question such that it results in a target answer. Previous works used a target answer to obtain a desired question. However, we also want to specify how to ask questions and improve the quality of generated questions. In this study, we explore the use of interrogative phrases as additional sources to control QG. By providing interrogative phrases, we expect that QG can generate a more reliable sequence of words subsequent to an interrogative phrase. We present a baseline sequence-to-sequence model with the attention, copy, and coverage mechanisms, and show that the simple baseline achieves state-of-the-art performance. The experiments demonstrate that interrogative phrases contribute to improving the performance of QG. In addition, we report the superiority of using interrogative phrases in human evaluation. Finally, we show that a question answering system can provide target answers more correctly when the questions are generated with interrogative phrases.</abstract>
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    <identifier type="doi">10.18653/v1/W19-8613</identifier>
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        <date>2019-oct–nov</date>
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%0 Conference Proceedings
%T Neural Question Generation using Interrogative Phrases
%A Sasazawa, Yuichi
%A Takase, Sho
%A Okazaki, Naoaki
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F sasazawa-etal-2019-neural
%X Question Generation (QG) is the task of generating questions from a given passage. One of the key requirements of QG is to generate a question such that it results in a target answer. Previous works used a target answer to obtain a desired question. However, we also want to specify how to ask questions and improve the quality of generated questions. In this study, we explore the use of interrogative phrases as additional sources to control QG. By providing interrogative phrases, we expect that QG can generate a more reliable sequence of words subsequent to an interrogative phrase. We present a baseline sequence-to-sequence model with the attention, copy, and coverage mechanisms, and show that the simple baseline achieves state-of-the-art performance. The experiments demonstrate that interrogative phrases contribute to improving the performance of QG. In addition, we report the superiority of using interrogative phrases in human evaluation. Finally, we show that a question answering system can provide target answers more correctly when the questions are generated with interrogative phrases.
%R 10.18653/v1/W19-8613
%U https://aclanthology.org/W19-8613
%U https://doi.org/10.18653/v1/W19-8613
%P 106-111

Markdown (Informal)

[Neural Question Generation using Interrogative Phrases](https://aclanthology.org/W19-8613) (Sasazawa et al., INLG 2019)

• Neural Question Generation using Interrogative Phrases (Sasazawa et al., INLG 2019)

ACL

• Yuichi Sasazawa, Sho Takase, and Naoaki Okazaki. 2019. Neural Question Generation using Interrogative Phrases. In Proceedings of the 12th International Conference on Natural Language Generation, pages 106–111, Tokyo, Japan. Association for Computational Linguistics.