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    <abstract>Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems. One natural type of question to ask tries to fill a gap in knowledge during text comprehension, like reading a news article: we might ask about background information, deeper reasons behind things occurring, or more. Despite recent progress with data-driven approaches, generating such questions is beyond the range of models trained on existing datasets. We introduce INQUISITIVE, a dataset of ~19K questions that are elicited while a person is reading through a document. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. We show that readers engage in a series of pragmatic strategies to seek information. Finally, we evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions although the task is challenging, and highlight the importance of context to generate INQUISITIVE questions.</abstract>
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%0 Conference Proceedings
%T Inquisitive Question Generation for High Level Text Comprehension
%A Ko, Wei-Jen
%A Chen, Te-yuan
%A Huang, Yiyan
%A Durrett, Greg
%A Li, Junyi Jessy
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ko-etal-2020-inquisitive
%X Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems. One natural type of question to ask tries to fill a gap in knowledge during text comprehension, like reading a news article: we might ask about background information, deeper reasons behind things occurring, or more. Despite recent progress with data-driven approaches, generating such questions is beyond the range of models trained on existing datasets. We introduce INQUISITIVE, a dataset of ~19K questions that are elicited while a person is reading through a document. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. We show that readers engage in a series of pragmatic strategies to seek information. Finally, we evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions although the task is challenging, and highlight the importance of context to generate INQUISITIVE questions.
%R 10.18653/v1/2020.emnlp-main.530
%U https://aclanthology.org/2020.emnlp-main.530
%U https://doi.org/10.18653/v1/2020.emnlp-main.530
%P 6544-6555

Markdown (Informal)

[Inquisitive Question Generation for High Level Text Comprehension](https://aclanthology.org/2020.emnlp-main.530) (Ko et al., EMNLP 2020)

• Inquisitive Question Generation for High Level Text Comprehension (Ko et al., EMNLP 2020)

ACL

• Wei-Jen Ko, Te-yuan Chen, Yiyan Huang, Greg Durrett, and Junyi Jessy Li. 2020. Inquisitive Question Generation for High Level Text Comprehension. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6544–6555, Online. Association for Computational Linguistics.