Evaluating Gender Bias in Natural Language Inference
- URL: http://arxiv.org/abs/2105.05541v1
- Date: Wed, 12 May 2021 09:41:51 GMT
- Title: Evaluating Gender Bias in Natural Language Inference
- Authors: Shanya Sharma, Manan Dey and Koustuv Sinha
- Abstract summary: We propose an evaluation methodology to measure gender bias in natural language understanding through inference.
We use our challenge task to investigate state-of-the-art NLI models on the presence of gender stereotypes using occupations.
Our findings suggest that three models trained on MNLI and SNLI datasets are significantly prone to gender-induced prediction errors.
- Score: 5.034017602990175
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Gender-bias stereotypes have recently raised significant ethical concerns in
natural language processing. However, progress in detection and evaluation of
gender bias in natural language understanding through inference is limited and
requires further investigation. In this work, we propose an evaluation
methodology to measure these biases by constructing a challenge task that
involves pairing gender-neutral premises against a gender-specific hypothesis.
We use our challenge task to investigate state-of-the-art NLI models on the
presence of gender stereotypes using occupations. Our findings suggest that
three models (BERT, RoBERTa, BART) trained on MNLI and SNLI datasets are
significantly prone to gender-induced prediction errors. We also find that
debiasing techniques such as augmenting the training dataset to ensure a
gender-balanced dataset can help reduce such bias in certain cases.
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