NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation

Sungdong Kim, Minsuk Chang, Sang-Woo Lee


Abstract
We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generates dialogues from (1) user’s goal instructions, which are the user context and task constraints in natural language, and (2) system’s API call results, which is a list of possible query responses for user requests from the given knowledge base. Labeler annotates the generated dialogue by formulating the annotation as a multiple-choice problem, in which the candidate labels are extracted from goal instructions and API call results. We demonstrate the effectiveness of the proposed method in the zero-shot domain transfer learning for dialogue state tracking. In the evaluation, the synthetic dialogue corpus generated from NeuralWOZ achieves a new state-of-the-art with improvements of 4.4% point joint goal accuracy on average across domains, and improvements of 5.7% point of zero-shot coverage against the MultiWOZ 2.1 dataset.
Anthology ID:
2021.acl-long.287
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3704–3717
Language:
URL:
https://aclanthology.org/2021.acl-long.287
DOI:
10.18653/v1/2021.acl-long.287
Bibkey:
Cite (ACL):
Sungdong Kim, Minsuk Chang, and Sang-Woo Lee. 2021. NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3704–3717, Online. Association for Computational Linguistics.
Cite (Informal):
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation (Kim et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.287.pdf
Video:
 https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.287.mp4
Code
 naver-ai/neuralwoz
Data
MultiWOZ