Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking

Takyoung Kim, Hoonsang Yoon, Yukyung Lee, Pilsung Kang, Misuk Kim


Abstract
Dialogue state tracking (DST) aims to extract essential information from multi-turn dialog situations and take appropriate actions. A belief state, one of the core pieces of information, refers to the subject and its specific content, and appears in the form of domain-slot-value. The trained model predicts “accumulated” belief states in every turn, and joint goal accuracy and slot accuracy are mainly used to evaluate the prediction; however, we specify that the current evaluation metrics have a critical limitation when evaluating belief states accumulated as the dialogue proceeds, especially in the most used MultiWOZ dataset. Additionally, we propose relative slot accuracy to complement existing metrics. Relative slot accuracy does not depend on the number of predefined slots, and allows intuitive evaluation by assigning relative scores according to the turn of each dialog. This study also encourages not solely the reporting of joint goal accuracy, but also various complementary metrics in DST tasks for the sake of a realistic evaluation.
Anthology ID:
2022.acl-short.33
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
297–309
Language:
URL:
https://aclanthology.org/2022.acl-short.33
DOI:
10.18653/v1/2022.acl-short.33
Bibkey:
Cite (ACL):
Takyoung Kim, Hoonsang Yoon, Yukyung Lee, Pilsung Kang, and Misuk Kim. 2022. Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 297–309, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking (Kim et al., ACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/improve-issue-templates/2022.acl-short.33.pdf
Video:
 https://preview.aclanthology.org/improve-issue-templates/2022.acl-short.33.mp4
Data
MultiWOZ