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Dictionary-based Debiasing of Pre-trained Word Embeddings
[article]
2021
arXiv
pre-print
Word embeddings trained on large corpora have shown to encode high levels of unfair discriminatory gender, racial, religious and ethnic biases. In contrast, human-written dictionaries describe the meanings of words in a concise, objective and an unbiased manner. We propose a method for debiasing pre-trained word embeddings using dictionaries, without requiring access to the original training resources or any knowledge regarding the word embedding algorithms used. Unlike prior work, our proposed
arXiv:2101.09525v1
fatcat:uqepsduj4zfebmngdqloefymyu