A Multi-cluster Boundary Learning Method for Out-of-Scope Intent Detection via MiniLM Embedding
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 boundar...
| Authors | Yihong Xu, Mingyu Kang, Linyuan Lü |
|---|---|
| Primary category | cs.CL |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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