Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework

2025-06-10

AI Summary

Intent detection suffers from strong dependence on in-domain labeled data and poor generalization to out-of-distribution (OOD) novel intents. While Generalized Intent Discovery (GID) aims to automatically discover unseen intents from unlabeled OOD data, existing approaches neglect cross-domain knowledge transfer, hindering effective reuse of prior domain knowledge for new intent identification. To address this, we propose a dual-driven paradigm integrating *prototype guidance* and *hierarchical consistency*: external prototypes preserve semantic priors from supervised models, while hierarchical consistency regularization, contrastive learning, and cross-domain feature alignment jointly enable robust knowledge transfer and novel intent discovery. Our method achieves significant improvements over state-of-the-art baselines across multiple benchmarks. The code is publicly available.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Intent detection aims to identify user intents from natural language inputs, where supervised methods rely heavily on labeled in-domain (IND) data and struggle with out-of-domain (OOD) intents, limiting their practical applicability. Generalized Intent Discovery (GID) addresses this by leveraging unlabeled OOD data to discover new intents without additional annotation. However, existing methods focus solely on clustering unsupervised data while neglecting domain adaptation. Therefore, we propose a consistency-driven prototype-prompting framework for GID from the perspective of integrating old and new knowledge, which includes a prototype-prompting framework for transferring old knowledge from external sources, and a hierarchical consistency constraint for learning new knowledge from target domains. We conducted extensive experiments and the results show that our method significantly outperforms all baseline methods, achieving state-of-the-art results, which strongly demonstrates the effectiveness and generalization of our methods. Our source code is publicly available at https://github.com/smileix/cpp.

Problem

• Detecting intents from natural language with limited labeled data
• Discovering new intents without additional annotation
• Integrating old and new knowledge for domain adaptation

Innovation

• Consistency-driven prototype-prompting framework
• Hierarchical consistency constraint learning
• Transferring old knowledge from external sources