Year: 2021
Type: conference-paper
Abstract: Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these.However, conversational questionanswering remains challenging due to the lack of realistic, domain-specific training data.Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes.We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity.Specifically, we design ChainCQG as a two-stage architecture that learns question-answer representations across multiple dialogue turns using a flow propagation training strategy.ChainCQG significantly outperforms both answer-aware and answer-unaware SOTA baselines (e.g., up to 48% BLEU-1 improvement).Additionally, our model is able to generate different types of questions, with improved fluidity and coreference alignment.
Authors: Jing Gu, Mostafa Mirshekari, Zhou Yu, Aaron Sisto
Institutions: University of California, Davis, Columbia University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3155952370