Papers by Daniel de Vassimon Manela
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models (2021.eacl-main)
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| Challenge: | Existing benchmarks do not probe professional bias as pronoun resolution may be obfuscated by cross-correlations from other manifestations of gender prejudice. |
| Approach: | They propose to use a skew and stereotype metrics to quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun resolution task. |
| Outcome: | The proposed methods reduce skew and stereotype relative to the unaugmented fine-tuned BERT model. |