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

09/10/2021
by   Li Zhou, et al.
0

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.

READ FULL TEXT

page 8

page 15

research
09/13/2019

Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering

Text-based Question Generation (QG) aims at generating natural and relev...
research
03/01/2023

DIFFQG: Generating Questions to Summarize Factual Changes

Identifying the difference between two versions of the same article is u...
research
10/20/2022

CONSISTENT: Open-Ended Question Generation From News Articles

Recent work on question generation has largely focused on factoid questi...
research
02/18/2021

Quiz-Style Question Generation for News Stories

A large majority of American adults get at least some of their news from...
research
10/19/2020

Question Generation for Supporting Informational Query Intents

Users frequently ask simple factoid questions when encountering question...
research
05/20/2021

ASQ: Automatically Generating Question-Answer Pairs using AMRs

In this work, we introduce ASQ, a tool to automatically mine questions a...
research
06/09/2020

ConfNet2Seq: Full Length Answer Generation from Spoken Questions

Conversational and task-oriented dialogue systems aim to interact with t...

Please sign up or login with your details

Forgot password? Click here to reset