Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts

Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang, Yongbin Li

Abstract
Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts. However, most previous OOD intent detection approaches are limited to single dialogue turns. In this paper, we introduce a context-aware OOD intent detection (Caro) framework to model multi-turn contexts in OOD intent detection tasks. Specifically, we follow the information bottleneck principle to extract robust representations from multi-turn dialogue contexts. Two different views are constructed for each input sample and the superfluous information not related to intent detection is removed using a multi-view information bottleneck loss. Moreover, we also explore utilizing unlabeled data in Caro. A two-stage training process is introduced to mine OOD samples from these unlabeled data, and these OOD samples are used to train the resulting model with a bootstrapping approach. Comprehensive experiments demonstrate that Caro establishes state-of-the-art performances on multi-turn OOD detection tasks by improving the F1-OOD score of over 29% compared to the previous best method.
Anthology ID:
2024.lrec-main.1097
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
12539–12552
Language:
URL:
https://aclanthology.org/2024.lrec-main.1097
DOI:
Bibkey:
Cite (ACL):
Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang, and Yongbin Li. 2024. Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12539–12552, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts (Lang et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/naacl24-info/2024.lrec-main.1097.pdf
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