| Challenge: | Experimental results show that our method consistently outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets, while maintaining high in-distance accuracy. |
| Approach: | They propose a minimax objective between a learner model being trained for the task and an auxiliary model aiming to maximize the learner's loss by up-weighting underrepresented "hard" examples with patterns that contradict the shortcuts learned from the prevailing "easy" examples. |
| Outcome: | The proposed method outperforms other robustness enhancement techniques on out-of-distribution adversarial test sets while maintaining high in-distance accuracy. |
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Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, Xia Hu
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| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
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Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance (2020.acl-main)
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| Challenge: | Neural models pick up on annotation artefacts and spurious correlations, resulting in learning sentences that suffer from the same biases. |
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Unsupervised training data re-weighting for natural language understanding with local distribution approximation (2022.emnlp-industry)
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Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities (2023.eacl-main)
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| Challenge: | Several proposals have been put forward for improving out-of-distribution performance by mitigating dataset biases. |
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