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    <abstract>Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with the protagonist in model-generated stories. Such biases may be expressed either explicitly (“women can’t park”) or implicitly (e.g. an unsolicited male character guides her into a parking space). We focus on implicit biases, and use a commonsense reasoning engine to uncover them. Specifically, we infer and analyze the protagonist’s motivations, attributes, mental states, and implications on others. Our findings regarding implicit biases are in line with prior work that studied explicit biases, for example showing that female characters’ portrayal is centered around appearance, while male figures’ focus on intellect.</abstract>
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%0 Conference Proceedings
%T Uncovering Implicit Gender Bias in Narratives through Commonsense Inference
%A Huang, Tenghao
%A Brahman, Faeze
%A Shwartz, Vered
%A Chaturvedi, Snigdha
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F huang-etal-2021-uncovering-implicit
%X Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with the protagonist in model-generated stories. Such biases may be expressed either explicitly (“women can’t park”) or implicitly (e.g. an unsolicited male character guides her into a parking space). We focus on implicit biases, and use a commonsense reasoning engine to uncover them. Specifically, we infer and analyze the protagonist’s motivations, attributes, mental states, and implications on others. Our findings regarding implicit biases are in line with prior work that studied explicit biases, for example showing that female characters’ portrayal is centered around appearance, while male figures’ focus on intellect.
%R 10.18653/v1/2021.findings-emnlp.326
%U https://aclanthology.org/2021.findings-emnlp.326
%U https://doi.org/10.18653/v1/2021.findings-emnlp.326
%P 3866-3873

Markdown (Informal)

[Uncovering Implicit Gender Bias in Narratives through Commonsense Inference](https://aclanthology.org/2021.findings-emnlp.326) (Huang et al., Findings 2021)

• Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (Huang et al., Findings 2021)

ACL

• Tenghao Huang, Faeze Brahman, Vered Shwartz, and Snigdha Chaturvedi. 2021. Uncovering Implicit Gender Bias in Narratives through Commonsense Inference. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3866–3873, Punta Cana, Dominican Republic. Association for Computational Linguistics.