Marion Bartl


Inferring Gender: A Scalable Methodology for Gender Detection with Online Lexical Databases
Marion Bartl | Susan Leavy
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

This paper presents a new method for automatic detection of gendered terms in large-scale language datasets. Currently, the evaluation of gender bias in natural language processing relies on the use of manually compiled lexicons of gendered expressions, such as pronouns and words that imply gender. However, manual compilation of lists with lexical gender can lead to static information if lists are not periodically updated and often involve value judgements by individual annotators and researchers. Moreover, terms not included in the lexicons fall out of the range of analysis.To address these issues, we devised a scalable dictionary-based method to automatically detect lexical gender that can provide a dynamic, up-to-date analysis with high coverage. Our approach reaches over 80% accuracy in determining the lexical gender of words retrieved randomly from a Wikipedia sample and when testing on a list of gendered words used in previous research.


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Unmasking Contextual Stereotypes: Measuring and Mitigating BERT’s Gender Bias
Marion Bartl | Malvina Nissim | Albert Gatt
Proceedings of the Second Workshop on Gender Bias in Natural Language Processing

Contextualized word embeddings have been replacing standard embeddings as the representational knowledge source of choice in NLP systems. Since a variety of biases have previously been found in standard word embeddings, it is crucial to assess biases encoded in their replacements as well. Focusing on BERT (Devlin et al., 2018), we measure gender bias by studying associations between gender-denoting target words and names of professions in English and German, comparing the findings with real-world workforce statistics. We mitigate bias by fine-tuning BERT on the GAP corpus (Webster et al., 2018), after applying Counterfactual Data Substitution (CDS) (Maudslay et al., 2019). We show that our method of measuring bias is appropriate for languages such as English, but not for languages with a rich morphology and gender-marking, such as German. Our results highlight the importance of investigating bias and mitigation techniques cross-linguistically,especially in view of the current emphasis on large-scale, multilingual language models.