Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble

• URL: http://arxiv.org/abs/2210.11034v1
• Date: Thu, 20 Oct 2022 06:05:58 GMT
• Title: Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble
• Authors: Hyunsoo Cho, Choonghyun Park, Jaewook Kang, Kang Min Yoo, Taeuk Kim, Sang-goo Lee
• Abstract summary: Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution. We propose a novel framework based on contrastive learning that encourages intermediate features to learn layer-specialized representations. Our approach is significantly more effective than other works.
• Score: 22.643719584452455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most recent studies in OOD detection utilize the information from a single representation that resides in the penultimate layer to determine whether the input is anomalous or not. Although such a method is straightforward, the potential of diverse information in the intermediate layers is overlooked. In this paper, we propose a novel framework based on contrastive learning that encourages intermediate features to learn layer-specialized representations and assembles them implicitly into a single representation to absorb rich information in the pre-trained language model. Extensive experiments in various intent classification and OOD datasets demonstrate that our approach is significantly more effective than other works.

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