On Measuring and Mitigating Biased Inferences of Word Embeddings

Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences. We use this observation to design a mechanism for measuring stereotypes using the task of natural language inference. We demonstrate a reduction in invalid inferences via bias mitigation strategies on static word embeddings (GloVe), and explore adapting them to contextual embeddings (ELMo).