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Are Pretrained Multilingual Models Equally Fair across Languages? (2022.coling-1)

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Challenge: Pretrained multilingual language models can help bridge the digital language divide, enabling high-quality NLP models for lower-resourced languages.
Approach: They propose to use a multilingual dataset to examine whether multilingual models are equally fair across languages.
Outcome: The proposed model enables apples-to-apples comparison across languages of group disparities in multilingual language models.

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