ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery

Jinggui Liang, Lizi Liao

Abstract
The discovery of new intent categories from user utterances is a crucial task in expanding agent skills. The key lies in how to efficiently solicit semantic evidence from utterances and properly transfer knowledge from existing intents to new intents. However, previous methods laid too much emphasis on relations among utterances or clusters for transfer learning, while paying less attention to the usage of semantics. As a result, these methods suffer from in-domain over-fitting and often generate meaningless new intent clusters due to data distortion. In this paper, we present a novel approach called Cluster Semantic Enhanced Prompt Learning (CsePL) for discovering new intents. Our method leverages two-level contrastive learning with label semantic alignment to learn meaningful representations of intent clusters. These learned intent representations are then utilized as soft prompt initializations for discriminating new intents, reducing the dominance of existing intents. Extensive experiments conducted on three public datasets demonstrate the superiority of our proposed method. It not only outperforms existing methods but also suggests meaningful intent labels and enables early detection of new intents.
Anthology ID:
2023.findings-emnlp.702
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10468–10481
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.702
DOI:
10.18653/v1/2023.findings-emnlp.702
Bibkey:
Cite (ACL):
Jinggui Liang and Lizi Liao. 2023. ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10468–10481, Singapore. Association for Computational Linguistics.
Cite (Informal):
ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery (Liang & Liao, Findings 2023)
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https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.702.pdf
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