Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey
Vevake Balaraman, Seyedmostafa Sheikhalishahi, Bernardo Magnini
Abstract
This paper aims at providing a comprehensive overview of recent developments in dialogue state tracking (DST) for task-oriented conversational systems. We introduce the task, the main datasets that have been exploited as well as their evaluation metrics, and we analyze several proposed approaches. We distinguish between static ontology DST models, which predict a fixed set of dialogue states, and dynamic ontology models, which can predict dialogue states even when the ontology changes. We also discuss the model’s ability to track either single or multiple domains and to scale to new domains, both in terms of knowledge transfer and zero-shot learning. We cover a period from 2013 to 2020, showing a significant increase of multiple domain methods, most of them utilizing pre-trained language models.- Anthology ID:
- 2021.sigdial-1.25
- Volume:
- Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- July
- Year:
- 2021
- Address:
- Singapore and Online
- Editors:
- Haizhou Li, Gina-Anne Levow, Zhou Yu, Chitralekha Gupta, Berrak Sisman, Siqi Cai, David Vandyke, Nina Dethlefs, Yan Wu, Junyi Jessy Li
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 239–251
- Language:
- URL:
- https://aclanthology.org/2021.sigdial-1.25
- DOI:
- 10.18653/v1/2021.sigdial-1.25
- Cite (ACL):
- Vevake Balaraman, Seyedmostafa Sheikhalishahi, and Bernardo Magnini. 2021. Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 239–251, Singapore and Online. Association for Computational Linguistics.
- Cite (Informal):
- Recent Neural Methods on Dialogue State Tracking for Task-Oriented Dialogue Systems: A Survey (Balaraman et al., SIGDIAL 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.sigdial-1.25.pdf
- Data
- MultiWOZ, SGD