Papers by Ewoenam Kwaku Tokpo

2 papers
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.
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 .

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