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We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The human-elicitation protocol employed in the construction of the SNLI makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples.
Rudinger et al. (Sun,) studied this question.
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