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.
CITATION STYLE
Rudinger, R., May, C., & van Durme, B. (2017). Social Bias in Elicited Natural Language Inferences. In EACL 2017 - Ethics in Natural Language Processing, Proceedings of the 1st ACL Workshop (pp. 74–79). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-1609
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