Papers by Toon Calders
Text Style Transfer for Bias Mitigation using Masked Language Modeling (2022.naacl-srw)
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| Challenge: | Various research findings have concluded that biased textual data has significant effects on target demographic groups. |
| Approach: | They propose a text-style transfer model that can be trained on non-parallel data and be used to automatically mitigate bias in textual data. |
| Outcome: | The proposed model improves on limitations of existing methods while maintaining good style transfer accuracy. |
Measuring Fairness with Biased Rulers: A Comparative Study on Bias Metrics for Pre-trained Language Models (2022.naacl-main)
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| Challenge: | An increasing awareness of biased patterns in natural language processing resources such as BERT has motivated many metrics to quantify ‘bias’ and ‘fairness’. |
| Approach: | They combine literature survey, correlation analysis and empirical evaluations to evaluate compatibility of fairness metrics for pre-trained language models and their downstream tasks. |
| Outcome: | The proposed measures are not compatible with each other and highly depend on (i) templates, (ii) attribute and target seeds and (iv) the choice of embeddings. |
How Far Can It Go? On Intrinsic Gender Bias Mitigation for Text Classification (2023.eacl-main)
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| Challenge: | a growing interest in exploring how gender bias pertains in contextualized language models has been generated . intrinsic mitigation strategies and bias metrics have been proposed to mitigate gender bias in contextualised language models . |
| Approach: | They propose to use different intrinsic bias mitigation strategies to mitigate gender bias in contextualized language models. |
| Outcome: | The proposed probe shows that some mitigation techniques can hide gender bias . the probe also shows that not all mitigation techniques fool extrinsic bias despite their use . |