DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation

Sarik Ghazarian, Zixi Liu, Tuhin Chakrabarty, Xuezhe Ma, Aram Galstyan, Nanyun Peng


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 to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, 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 produce 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 towards topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.
Anthology ID:
2021.naacl-demos.4
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations
Month:
June
Year:
2021
Address:
Online
Editors:
Avi Sil, Xi Victoria Lin
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
26–34
Language:
URL:
https://aclanthology.org/2021.naacl-demos.4
DOI:
10.18653/v1/2021.naacl-demos.4
Bibkey:
Cite (ACL):
Sarik Ghazarian, Zixi Liu, Tuhin Chakrabarty, Xuezhe Ma, Aram Galstyan, and Nanyun Peng. 2021. DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, pages 26–34, Online. Association for Computational Linguistics.
Cite (Informal):
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation (Ghazarian et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-demos.4.pdf
Supplementary:
 2021.naacl-demos.4.Supplementary.mp4
Video:
 https://aclanthology.org/2021.naacl-demos.4.mp4
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