Generalized Intent Discovery: Learning from Open World Dialogue System

Yutao Mou, Keqing He, Yanan Wu, Pei Wang, Jingang Wang, Wei Wu, Yi Huang, Junlan Feng, Weiran Xu


Abstract
Traditional intent classification models are based on a pre-defined intent set and only recognize limited in-domain (IND) intent classes. But users may input out-of-domain (OOD) queries in a practical dialogue system. Such OOD queries can provide directions for future improvement. In this paper, we define a new task, Generalized Intent Discovery (GID), which aims to extend an IND intent classifier to an open-world intent set including IND and OOD intents. We hope to simultaneously classify a set of labeled IND intent classes while discovering and recognizing new unlabeled OOD types incrementally. We construct three public datasets for different application scenarios and propose two kinds of frameworks, pipeline-based and end-to-end for future work. Further, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide new guidance for future GID research.
Anthology ID:
2022.coling-1.59
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
707–720
Language:
URL:
https://aclanthology.org/2022.coling-1.59
DOI:
Bibkey:
Cite (ACL):
Yutao Mou, Keqing He, Yanan Wu, Pei Wang, Jingang Wang, Wei Wu, Yi Huang, Junlan Feng, and Weiran Xu. 2022. Generalized Intent Discovery: Learning from Open World Dialogue System. In Proceedings of the 29th International Conference on Computational Linguistics, pages 707–720, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Generalized Intent Discovery: Learning from Open World Dialogue System (Mou et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.59.pdf
Code
 myt517/gid_benchmark