Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
Li Zhou, Kevin Small, Yong Zhang, Sandeep Atluri · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with selfcontained, 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.1