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        <title>Let Me Know What to Ask: Interrogative-Word-Aware Question Generation</title>
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            <title>Proceedings of the 2nd Workshop on Machine Reading for Question Answering</title>
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            <namePart type="given">Adam</namePart>
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    <abstract>Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generating a question based on a text and possibly the answer to the generated question. They need to determine the type of interrogative word to be generated while having to pay attention to the grammar and vocabulary of the question. In this work, we propose Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system composed of two modules: an interrogative word classifier and a QG model. The first module predicts the interrogative word that is provided to the second module to create the question. Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in METEOR, and from 44.53 to 46.94 in ROUGE-L.</abstract>
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%0 Conference Proceedings
%T Let Me Know What to Ask: Interrogative-Word-Aware Question Generation
%A Kang, Junmo
%A Puerto San Roman, Haritz
%A Myaeng, Sung-Hyon
%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 kang-etal-2019-know
%X Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generating a question based on a text and possibly the answer to the generated question. They need to determine the type of interrogative word to be generated while having to pay attention to the grammar and vocabulary of the question. In this work, we propose Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system composed of two modules: an interrogative word classifier and a QG model. The first module predicts the interrogative word that is provided to the second module to create the question. Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in METEOR, and from 44.53 to 46.94 in ROUGE-L.
%R 10.18653/v1/D19-5822
%U https://aclanthology.org/D19-5822/
%U https://doi.org/10.18653/v1/D19-5822
%P 163-171

Markdown (Informal)

[Let Me Know What to Ask: Interrogative-Word-Aware Question Generation](https://aclanthology.org/D19-5822/) (Kang et al., 2019)

• Let Me Know What to Ask: Interrogative-Word-Aware Question Generation (Kang et al., 2019)

ACL

• Junmo Kang, Haritz Puerto San Roman, and Sung-Hyon Myaeng. 2019. Let Me Know What to Ask: Interrogative-Word-Aware Question Generation. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 163–171, Hong Kong, China. Association for Computational Linguistics.