Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai, Yi Zhang


Abstract
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we present PPTOD, a unified plug-and-play model for task-oriented dialogue. In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora. We extensively test our model on three benchmark TOD tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification. Experimental results show that PPTOD achieves new state of the art on all evaluated tasks in both high-resource and low-resource scenarios. Furthermore, comparisons against previous SOTA methods show that the responses generated by PPTOD are more factually correct and semantically coherent as judged by human annotators.
Anthology ID:
2022.acl-long.319
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4661–4676
Language:
URL:
https://aclanthology.org/2022.acl-long.319
DOI:
10.18653/v1/2022.acl-long.319
Bibkey:
Cite (ACL):
Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai, and Yi Zhang. 2022. Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4661–4676, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (Su et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.319.pdf
Software:
 2022.acl-long.319.software.zip
Video:
 https://aclanthology.org/2022.acl-long.319.mp4
Code
 awslabs/pptod