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Towards Textual Out-of-Domain Detection without any In-Domain Labels
Di Jin · Shuyang Gao · Seokhwan Kim · Yang Liu · Dilek Hakkani-Tur

In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions. This work focuses on a challenging case of OOD detection, where no labels for in-domain data are accessible (e.g., no intent labels for the intent classification task). To this end, we propose a novel representation learning based method by combining unsupervised clustering and contrastive learning so that better data representations for OOD detection can be learned. Through extensive experiments, we demonstrate that this method can be even competitive to the state-of-the-art supervised approaches with label information.

Author Information

Di Jin (Amazon Alexa AI)
Shuyang Gao (Amazon)
Seokhwan Kim (Amazon Alexa AI)
Yang Liu (Amazon)

- 2004 Ph.d -2005-2018 professor at Univ. of Texas at Dallas - 2015-2016 visiting research scientist at Google research - 2016-2017 research scientist at Facebook - 2017-2019 Head of LAIX AI lab - 2019-present principal scientist at Amazon, Alexa AI

Dilek Hakkani-Tur (Amazon Alexa AI)

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