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Assessing Social and Intersectional Biases in Contextualized Word Representations

Yi Chern Tan, L. Elisa Celis

Neural Information Processing Systems

Socialbiasinmachine learning hasdrawnsignificant attention, withworkranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias. In natural language processing, gender bias has been shown to exist in context-free word embeddings. Recently, contextual word representations have outperformed word embeddings in several downstream NLP tasks.