Social Bias in Elicited Natural Language Inferences

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Abstract

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.

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APA

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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