Social Bias in Elicited Natural Language Inferences

2017-04-01WS 2017Code Available0· sign in to hype

Rachel Rudinger, Ch May, ler, Benjamin Van Durme

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github.com/cjmay/snli-ethics
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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 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.

Tasks

Language Modeling Language Modelling Natural Language Inference Word Embeddings

Social Bias in Elicited Natural Language Inferences

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Abstract

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