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GeBNLP - 2022
Total Papers:- 29
Total Papers accross all years:- 82
Total Citations :- 0
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Occupational Biases in Norwegian and Multilingual Language Models
Samia Touileb |
Lilja Øvrelid |
Erik Velldal |
Mitigating Gender Stereotypes in Hindi and Marathi
Neeraja Kirtane |
Tanvi Anand |
Why Knowledge Distillation Amplifies Gender Bias and How to Mitigate from the Perspective of DistilBERT
Jaimeen Ahn |
Hwaran Lee |
Jinhwa Kim |
Alice Oh |
Choose Your Lenses: Flaws in Gender Bias Evaluation
Hadas Orgad |
Yonatan Belinkov |
On the Dynamics of Gender Learning in Speech Translation
Beatrice Savoldi |
Marco Gaido |
Luisa Bentivogli |
Matteo Negri |
Marco Turchi |
Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements
Conrad Borchers |
Dalia Gala |
Benjamin Gilburt |
Eduard Oravkin |
Wilfried Bounsi |
Yuki M Asano |
Hannah Kirk |
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings
Jiali Li |
Shucheng Zhu |
Ying Liu |
Pengyuan Liu |
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
Christian Hardmeier |
Christine Basta |
Marta R. Costa-jussà |
Gabriel Stanovsky |
Hila Gonen |
Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruning
Przemyslaw Joniak |
Akiko Aizawa |
Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text
Lucy Havens |
Beatrice Alex |
Benjamin Bach |
Melissa Terras |
On Gender Biases in Offensive Language Classification Models
Sanjana Marcé |
Adam Poliak |
Incorporating Subjectivity into Gendered Ambiguous Pronoun (GAP) Resolution using Style Transfer
Kartikey Pant |
Tanvi Dadu |
Don’t Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information
Tomasz Limisiewicz |
David Mareček |
The Birth of Bias: A case study on the evolution of gender bias in an English language model
Oskar Van Der Wal |
Jaap Jumelet |
Katrin Schulz |
Willem Zuidema |
Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models
Tejas Srinivasan |
Yonatan Bisk |
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