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

Hyunsoo Cho, Choonghyun Park, Jaewook Kang, Kang Min Yoo, Taeuk Kim, Sang-goo Lee

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.
Anthology ID:
2022.findings-emnlp.55
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
783–798
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.55
DOI:
10.18653/v1/2022.findings-emnlp.55
Bibkey:
Cite (ACL):
Hyunsoo Cho, Choonghyun Park, Jaewook Kang, Kang Min Yoo, Taeuk Kim, and Sang-goo Lee. 2022. Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 783–798, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble (Cho et al., Findings 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.findings-emnlp.55.pdf
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