Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems

Derek Chen, Howard Chen, Yi Yang, Alexander Lin, Zhou Yu


Abstract
Existing goal-oriented dialogue datasets focus mainly on identifying slots and values. However, customer support interactions in reality often involve agents following multi-step procedures derived from explicitly-defined company policies as well. To study customer service dialogue systems in more realistic settings, we introduce the Action-Based Conversations Dataset (ABCD), a fully-labeled dataset with over 10K human-to-human dialogues containing 55 distinct user intents requiring unique sequences of actions constrained by policies to achieve task success. We propose two additional dialog tasks, Action State Tracking and Cascading Dialogue Success, and establish a series of baselines involving large-scale, pre-trained language models on this dataset. Empirical results demonstrate that while more sophisticated networks outperform simpler models, a considerable gap (50.8% absolute accuracy) still exists to reach human-level performance on ABCD.
Anthology ID:
2021.naacl-main.239
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3002–3017
Language:
URL:
https://aclanthology.org/2021.naacl-main.239
DOI:
10.18653/v1/2021.naacl-main.239
Bibkey:
Cite (ACL):
Derek Chen, Howard Chen, Yi Yang, Alexander Lin, and Zhou Yu. 2021. Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3002–3017, Online. Association for Computational Linguistics.
Cite (Informal):
Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems (Chen et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.239.pdf
Code
 asappresearch/abcd
Data
ABCD