This paper proposes a semantic-enhanced Gaussian mixture model (SEG) for unknown intent detection.
we model utterance embeddings with a Gaussian mixture distribution and inject dynamic class semantic information into Gaussian means, which enables learning more classconcentrated embeddings that help to facilitate downstream outlier detection.