Social Bias in Elicited Natural Language Inferences

WS 2017  ·  Rachel Rudinger, Ch May, ler, Benjamin Van Durme ·

We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The SNLI human-elicitation protocol 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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