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cs.CL2020
Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings
Vaibhav Kumar, Tenzin Singhay Bhotia
Non-contextual word embedding models have been shown to inherit human-like stereotypical biases of gender, race and religion from the training corpora. To counter this issue, a lar…
cs.CL2020★ 1 cited
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings
Vaibhav Kumar, Tenzin Singhay Bhotia, Tanmoy Chakraborty
Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations res…