Papers by William Shand

1 papers
Conditional Supervised Contrastive Learning for Fair Text Classification (2022.findings-emnlp)

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Challenge: Recent advances in natural language processing have demonstrated societal bias in existing NLP models.
Approach: They propose to use contrastive learning to learn fair representations for text classification . they conduct experiments on two text datasets to demonstrate their methods are stable .
Outcome: The proposed methods balancing task performance and bias mitigation are stable in different hyperparameter settings.

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