Title
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation
Abstract
Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded in generating fluent and human-like responses. However, those systems still lack control over the generation process toward producing contentful responses and achieving engaging conversations. To address this, we present DiSCoL (Dialogue Systems through Coversational Line guided response generation). DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly convlines) as controllable and informative content-planning elements to guide the generation model in producing engaging and informative responses. Two primary modules in DiSCoL's pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines. Users can also change the returned convlines to control the direction of the conversations toward topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.
Year
DOI
Venue
2021
10.18653/v1/2021.naacl-demos.4
2021 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES: DEMONSTRATIONS (NAACL-HLT 2021)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Sarik Ghazarian111.37
Zixi Liu200.68
Tuhin Chakrabarty314.74
Xuezhe Ma434321.76
Aram Galstyan5103394.05
Nanyun Peng615528.78