Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

Biswesh Mohapatra, Gaurav Pandey, Danish Contractor, Sachindra Joshi


Abstract
Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - MultiWOZ dataset and the Persona chat dataset.
Anthology ID:
2021.findings-emnlp.103
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1190–1203
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.103
DOI:
10.18653/v1/2021.findings-emnlp.103
Bibkey:
Cite (ACL):
Biswesh Mohapatra, Gaurav Pandey, Danish Contractor, and Sachindra Joshi. 2021. Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1190–1203, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions (Mohapatra et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.103.pdf
Data
MultiWOZPAWS