HiIntent: A Collaborative Hierarchical Framework for Zero-Shot Intent Detection

Shuo Wang, Zeyu Wei, Wenjing Chang, Guangjun Shi, Jianjun Yu

Abstract: In recent years, single-label intent recognition has faced significant challenges in handling short and semantically sparse user inputs. Traditional methods often treat intent labels as flat categories, neglecting their inherent hierarchical relationships and limiting model performance. To address these issues, we propose HiIntent , a novel zero-shot intent detection framework that integrates hierarchical semantic modeling with a collaborative generation-discriminative mechanism. HiIntent first constructs a structured label hierarchy through a two-stage process: a large language model (LLM) generates semantic abstractions of intent labels, which are then evaluated and refined by a discriminative module to ensure coherence and correctness. This is followed by a similarity-driven convergence strategy that enhances intra-class consistency and inter-class separability using multi-metric similarity calculations. Finally, a contrastive prompt construction method leverages the learned label hierarchy to generate enriched semantic descriptions for each intent, improving representation learning and facilitating accurate classification even in zero-shot scenarios. Extensive experiments on both general-purpose (CLINC-150) and domain-specific (RFMR) datasets demonstrate that HiIntent consistently outperforms existing approaches across multiple evaluation metrics. Ablation studies and hyperparameter analyses further validate the effectiveness of each component in the proposed framework.

External IDs:dblp:conf/smc/WangWCSY25