Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems

Sandesh Swamy, Narges Tabari, Chacha Chen, Rashmi Gangadharaiah


Abstract
Response generation is one of the critical components in task-oriented dialog systems. Existing studies have shown that large pre-trained language models can be adapted to this task. The typical paradigm of adapting such extremely large language models would be by fine-tuning on the downstream tasks which is not only time-consuming but also involves significant resources and access to fine-tuning data. Prompting (Schick and Schütze, 2020) has been an alternative to fine-tuning in many NLP tasks. In our work, we explore the idea of using prompting for response generation in task-oriented dialog systems. Specifically, we propose an approach that performs contextual dynamic prompting where the prompts are learnt from dialog contexts. We aim to distill useful prompting signals from the dialog context. On experiments with MultiWOZ 2.2 dataset (Zang et al., 2020), we show that contextual dynamic prompts improve response generation in terms of combined score (Mehri et al., 2019) by 3 absolute points, and an additional 17 points when dialog states are incorporated. Furthermore, we carried out human annotation on these conversations and found that agents which incorporate context are preferred over agents with vanilla prefix-tuning.
Anthology ID:
2023.eacl-main.226
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3102–3111
Language:
URL:
https://aclanthology.org/2023.eacl-main.226
DOI:
10.18653/v1/2023.eacl-main.226
Bibkey:
Cite (ACL):
Sandesh Swamy, Narges Tabari, Chacha Chen, and Rashmi Gangadharaiah. 2023. Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3102–3111, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems (Swamy et al., EACL 2023)
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PDF:
https://aclanthology.org/2023.eacl-main.226.pdf
Video:
 https://aclanthology.org/2023.eacl-main.226.mp4