Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning

• URL: http://arxiv.org/abs/2109.04689v1
• Date: Fri, 10 Sep 2021 06:34:55 GMT
• Title: Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
• Authors: Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri
• Abstract summary: We propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions.
• Score: 7.2745835227138045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.

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