@inproceedings{cho-etal-2022-enhancing,
title = "Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble",
author = "Cho, Hyunsoo and
Park, Choonghyun and
Kang, Jaewook and
Yoo, Kang Min and
Kim, Taeuk and
Lee, Sang-goo",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.55",
doi = "10.18653/v1/2022.findings-emnlp.55",
pages = "783--798",
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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<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.</abstract>
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%0 Conference Proceedings
%T Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble
%A Cho, Hyunsoo
%A Park, Choonghyun
%A Kang, Jaewook
%A Yoo, Kang Min
%A Kim, Taeuk
%A Lee, Sang-goo
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F cho-etal-2022-enhancing
%X 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.
%R 10.18653/v1/2022.findings-emnlp.55
%U https://aclanthology.org/2022.findings-emnlp.55
%U https://doi.org/10.18653/v1/2022.findings-emnlp.55
%P 783-798
Markdown (Informal)
[Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble](https://aclanthology.org/2022.findings-emnlp.55) (Cho et al., Findings 2022)
ACL