Comparing form-based and meaning-based gender biases in pronoun resolution: Inferences from names and role nouns

Referring to people using only their name (e.g. Jones came in) is known to evoke the assumption that the individual is male. The same holds with some role nouns (e.g. mechanic, boxer). We explore these effects through the lens of pronoun interpretation in English. In two sentence-completion studies, we show that both form-based (last-name-only style) and meaning-based gender biases (from role nouns) are powerful enough to eliminate otherwise robust verb semantic effects on pronoun interpretation (implicit causality). In addition, the results provide initial evidence that meaning-based biases (at least the ones tested here) can be stronger than form-based biases, which may stem from differences in the form-function mapping.

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Publication Details

Journal
Proceedings of the Linguistic Society of America
Published
2025-06-18
DOI
https://doi.org/10.3765/plsa.v10i1.5963
Primary Topic
Natural Language Processing Techniques
Type
article
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article

Comparing form-based and meaning-based gender biases in pronoun resolution: Inferences from names and role nouns

Elsi Kaiser
Proceedings of the Linguistic Society of America
Natural Language Processing Techniques
article

Comparing form-based and meaning-based gender biases in pronoun resolution: Inferences from names and role nouns

Elsi Kaiser
article en

Abstract

Referring to people using only their name (e.g. Jones came in) is known to evoke the assumption that the individual is male. The same holds with some role nouns (e.g. mechanic, boxer). We explore these effects through the lens of pronoun interpretation in English. In two sentence-completion studies, we show that both form-based (last-name-only style) and meaning-based gender biases (from role nouns) are powerful enough to eliminate otherwise robust verb semantic effects on pronoun interpretation (implicit causality). In addition, the results provide initial evidence that meaning-based biases (at least the ones tested here) can be stronger than form-based biases, which may stem from differences in the form-function mapping.

Proceedings of the Linguistic Society of AmericaVol. 10(1)
University of Southern California (US), California Southern University (US)
Openalex Percentile: Top 93%
Natural Language Processing Techniques
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