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        <title>Question-type Driven Question Generation</title>
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        <namePart type="given">Wenjie</namePart>
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        <namePart type="given">Minghua</namePart>
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    <name type="personal">
        <namePart type="given">Yunfang</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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        <name type="personal">
            <namePart type="given">Jing</namePart>
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    <abstract>Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type how while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1622</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Question-type Driven Question Generation
%A Zhou, Wenjie
%A Zhang, Minghua
%A Wu, Yunfang
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhou-etal-2019-question
%X Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type how while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.
%R 10.18653/v1/D19-1622
%U https://aclanthology.org/D19-1622
%U https://doi.org/10.18653/v1/D19-1622
%P 6032-6037

Markdown (Informal)

[Question-type Driven Question Generation](https://aclanthology.org/D19-1622) (Zhou et al., EMNLP-IJCNLP 2019)

• Question-type Driven Question Generation (Zhou et al., EMNLP-IJCNLP 2019)

ACL

• Wenjie Zhou, Minghua Zhang, and Yunfang Wu. 2019. Question-type Driven Question Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6032–6037, Hong Kong, China. Association for Computational Linguistics.