From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning

Feng Zhang, Wei Chen, Fei Ding, Meng Gao, Tengjiao Wang, Jiahui Yao, Jiabin Zheng

Abstract
Intent detection aims to identify user goals from utterances, and is a ubiquitous step towards the satisfaction of user desired needs in many interaction systems. As dynamic and varied intents arise, models that are capable of identifying new intents promptly are required. However, existing studies usually fine-tune discriminative models on the specific defined intent classes, precluding them from being directly adopted to new intent domains. In this paper, we introduce a generative pre-trained intent model that can recognize new intents from different domains in low-resource scenarios. We reformulate intent detection into a generation task and design descriptive and regularized instructions to guide the model effectively to detect new intents in open domains with no parameter updates. To validate the proposed method, we introduce a new intent detection benchmark, including the Meta-Intent Dataset and three types of representative evaluation settings. We conduct extensive experiments which demonstrate that our method outperforms a range of strong baselines that needs further fine-tuning or domain-specific samples.
Anthology ID:
2024.findings-acl.605
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10167–10183
Language:
URL:
https://aclanthology.org/2024.findings-acl.605
DOI:
Bibkey:
Cite (ACL):
Feng Zhang, Wei Chen, Fei Ding, Meng Gao, Tengjiao Wang, Jiahui Yao, and Jiabin Zheng. 2024. From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning. In Findings of the Association for Computational Linguistics ACL 2024, pages 10167–10183, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (Zhang et al., Findings 2024)
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