Reinforced Dynamic Reasoning for Conversational Question Generation
Boyuan Pan, Hao Li, Ziyu Yao, Deng Cai, Huan Sun · 2019
This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turns of question-answer pairs).CQG is a crucial task for developing intelligent agents that can drive question-answering style conversations or test user understanding of a given passage.Towards that end, we propose a new approach named Reinforced Dynamic Reasoning (ReDR) network, which is based on the general encoder-decoder framework but incorporates a reasoning procedure in a dynamic manner to better understand what has been asked and what to ask next about the passage.To encourage producing meaningful questions, we leverage a popular question answering (QA) model to provide feedback and fine-tune the question generator using a reinforcement learning mechanism.Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants.Moreover, to show the applicability of our method, we also apply it to create multiturn question-answering conversations for passages in SQuAD. * Work done while visiting the Ohio State University.Shelly is in second grade.She is a new student at her school.Shelly's family has lived in many different places.Shelly was born in Florida.Her family moved to Tennessee when she was two years old.When she was four years old, they moved to Texas.They moved from there to Arizona, where they now live.Q1: What grade is Shelly in ?A1: second R1: Shelly is in second grade.Q2: Was she a