Towards Stable Natural Language Understanding via Information Entropy Guided Debiasing (2023.acl-long)
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| Challenge: | Existing approaches to debiase Natural Language Understanding models use dataset biases instead of learning the intended task. |
| Approach: | They propose a debiasing framework that detects and purifies dataset biases using information entropy. |
| Outcome: | The proposed framework improves the stability of performance on out-of-distribution datasets for a set of widely adopted NLU models. |
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