29 citations · 89 across the 11 of their papers we have counts for
11 papers
FairFlow: An Automated Approach to Model-based Counterfactual Data Augmentation For NLP
Ewoenam Kwaku Tokpo, Toon Calders
Despite the evolution of language models, they continue to portray harmful societal biases and stereotypes inadvertently learned from training data. These inherent biases often res…
Model-based Counterfactual Generator for Gender Bias Mitigation
Ewoenam Kwaku Tokpo, Toon Calders
Counterfactual Data Augmentation (CDA) has been one of the preferred techniques for mitigating gender bias in natural language models. CDA techniques have mostly employed word subs…
How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification
Ewoenam Tokpo, Pieter Delobelle, Bettina Berendt +1
To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use o…
Text Style Transfer for Bias Mitigation using Masked Language Modeling
Ewoenam Kwaku Tokpo, Toon Calders
It is well known that textual data on the internet and other digital platforms contain significant levels of bias and stereotypes. Although many such texts contain stereotypes and…
Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models
Pieter Delobelle, Ewoenam Kwaku Tokpo, Toon Calders +1
An increasing awareness of biased patterns in natural language processing resources, like BERT, has motivated many metrics to quantify `bias' and `fairness'. But comparing the resu…
Electron Bessel beam diffraction for precise and accurate nanoscale strain mapping
Giulio Guzzinati, Wannes Ghielens, Christoph Mahr +4
Strain has a strong effect on the properties of materials and the performance of electronic devices. Their ever shrinking size translates into a constant demand for accurate and pr…