Few-Shot Out of Domain Intent Detection with Covariance Corrected Mahalanobis Distance

Abstract:Conversational agents like chatbots and voice assistants are trained to understand and respond to user intents. On encountering an utterance with an intent different from the ones they have been trained on, these agents are expected to classify the intent as `unknown' or `out of domain'. This problem is known as out of domain (OOD) intent detection. Podolskiy et al. (2021), showed that Mahalanobis distance can be used effectively for identifying OOD intents, outperforming competing approaches. However, their method fails to outperform the baselines in the practically important few-shot setting. In this paper we analyze the reason for low performance and propose a covariance corrected Mahalanobis distance for detecting out-of-domain intents.

Artificial Intelligence (cs.AI)

arXiv:2609.00961 [cs.AI]

(or arXiv:2609.00961v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.00961 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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