Inquisitive Question Generation for High Level Text Comprehension

• URL: http://arxiv.org/abs/2010.01657v1
• Date: Sun, 4 Oct 2020 19:03:39 GMT
• Title: Inquisitive Question Generation for High Level Text Comprehension
• Authors: Wei-Jen Ko and Te-Yuan Chen and Yiyan Huang and Greg Durrett and Junyi Jessy Li
• Abstract summary: We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
• Score: 60.21497846332531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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