Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection

نویسندگان

چکیده

Real-life applications, heavily relying on machine learning, such as dialog systems, demand for out-of-domain detection methods. Intent classification models should be equipped with a mechanism to distinguish seen intents from unseen ones so that the agent is capable of rejecting latter and avoiding undesired behavior. However, despite increasing attention paid task, best practices intent have not yet been fully established. This paper conducts thorough comparison We prioritize methods, requiring access data during training, gathering which extremely time- labor-consuming due lexical stylistic variation user utterances. evaluate multiple contextual encoders proven efficient, three common datasets classification, expanded Our main findings show fine-tuning Transformer-based in-domain leads superior results. Mahalanobis distance, together utterance representations, derived encoders, outperform other methods by wide margin(1-5% in terms AUROC) establish new state-of-the-art results all datasets. The broader analysis shows reason success lies fact fine-tuned Transformer constructing homogeneous representations utterances, revealing geometrical disparity out domain In turn, distance captures this easily.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2021

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v35i15.17612