A Multi-cluster Boundary Learning Method for Out-of-Scope Intent Detection via MiniLM Embedding

2026-07-08

AI Summary

This work addresses the performance degradation of existing out-of-scope intent detection methods as the number of known intent classes increases, as well as the high computational cost and deployment challenges associated with large language model–based embeddings. To overcome these limitations, the authors propose a lightweight and efficient approach that leverages MiniLM (all-MiniLM-L6-v2) embeddings within a one-class classification framework augmented with a multi-cluster boundary learning mechanism. This mechanism explicitly models the semantic cluster structure of in-scope training utterances to better characterize the known intent distribution, thereby improving the rejection of out-of-domain samples. The method achieves state-of-the-art performance on CLINC150, StackOverflow, and Banking77 benchmarks. Ablation studies confirm the synergistic benefits of combining MiniLM embeddings with multi-cluster boundaries, demonstrating significantly enhanced out-of-scope detection while maintaining low computational overhead.

Abstract

Intent detection is a critical task that bridges human intents and system actions in human-machine interaction systems. However, there still exist challenges for detecting out-of-scope (OOS) intents. (i) The traditional methods view the OOS intent detection as a multi-class classification, then the detection accuracy decreases as the class number of the known intents increases; (ii) LLM-embedding methods require large parameters, that makes them difficult to train and practically deploy. Thus, this work proposes a multi-cluster boundary learning method to detect OOS intents via MiniLM embedding (i.e., all-MiniLM-L6-v2) in an one-class classification workflow. The method learns the boundaries of multi-cluster embeddings generated by MiniLM from the training utterances, and then rejects the out-of-domain utterances as OOS intents. Experiments are conducted on public CLINC150, StackOverflow and Banking77 datasets. The results show that the method achieves the state-of-the-art OOS intent detection performance compared the other baselines. Ablation studies are also conducted and the results show that the used MiniLM can better adapt to the workflow and utterance embedding requirements. The code is available at supplementary materials.

Problem

• out-of-scope intent detection
• intent detection
• MiniLM embedding
• one-class classification
• human-machine interaction

Innovation

• out-of-scope intent detection
• multi-cluster boundary learning
• MiniLM embedding
• one-class classification
• intent detection