kFolden: k-Fold Ensemble for Out-Of-Distribution Detection

Xiaoya Li, Jiwei Li, Xiaofei Sun, Chun Fan, Tianwei Zhang, Fei Wu, Yuxian Meng, Jun Zhang


Abstract
Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP). In this work, we propose a simple yet effective framework kFolden, which mimics the behaviors of OOD detection during training without the use of any external data. For a task with k training labels, kFolden induces k sub-models, each of which is trained on a subset with k-1 categories with the left category masked unknown to the sub-model. Exposing an unknown label to the sub-model during training, the model is encouraged to learn to equally attribute the probability to the seen k-1 labels for the unknown label, enabling this framework to simultaneously resolve in- and out-distribution examples in a natural way via OOD simulations. Taking text classification as an archetype, we develop benchmarks for OOD detection using existing text classification datasets. By conducting comprehensive comparisons and analyses on the developed benchmarks, we demonstrate the superiority of kFolden against current methods in terms of improving OOD detection performances while maintaining improved in-domain classification accuracy.
Anthology ID:
2021.emnlp-main.248
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3102–3115
Language:
URL:
https://aclanthology.org/2021.emnlp-main.248
DOI:
10.18653/v1/2021.emnlp-main.248
Bibkey:
Cite (ACL):
Xiaoya Li, Jiwei Li, Xiaofei Sun, Chun Fan, Tianwei Zhang, Fei Wu, Yuxian Meng, and Jun Zhang. 2021. kFolden: k-Fold Ensemble for Out-Of-Distribution Detection-Fold Ensemble for Out-Of-Distribution Detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3102–3115, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection-Fold Ensemble for Out-Of-Distribution Detection (Li et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.248.pdf
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