Gender Bias and Sexism in Language
By Michela Menegatti, M. Rubini. 2017
102 citations recorded by the source corpus.
Subfields: Gender Studies, Natural Language Processing, Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Gender Studies
- J. M. Serrano
- Marta Ruiz Costa-jussà
- Hal Daumé Iii
- Hanna M. Wallach
- Orestis Papakyriakopoulos
- F. Marco
- Simon Hegelich
In Natural Language Processing
- Hal Daumé Iii
- Marta Ruiz Costa-jussà
- Nizar Habash
- Houda Bouamor
- Su Lin Blodgett
- Bashar Alhafni
- J. M. Serrano
- Hanna M. Wallach
In Safety, Robustness & Interpretability
- Su Lin Blodgett
- Marta Ruiz Costa-jussà
- Hal Daumé Iii
- Nizar Habash
- Houda Bouamor
- Hanna M. Wallach
- J. M. Serrano
- Orestis Papakyriakopoulos
Citations in the corpus
- Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “…changes through changes in language, disrupting patterns of oppression and marginalization via so-called “gender-fair” language (Sczesny et al., 2016; Menegatti and Rubini, 2017), language that is more inclusive to people with disabilities (ADA, 2018), and language that is less dehumanizing (e.g.,…”
- Bias in word embeddings (2020, background): “[32, 73], while we defined and searched sex related compliments given theories of benevolent sexism [9, 59].”
- Gender-Aware Reinflection using Linguistically Enhanced Neural Models (2020, background): “Human-generated data, reflective of the gender discrimination and sexist stereotypes perpetrated through language and speaker’s lexical choices, is considered the primary source of these biases (Maass and Arcuri, 1996; Menegatti and Rubini, 2017).”
- An analysis of gender bias studies in natural language processing (2019, background): “Languages and cultures encode gender differently: some ignore gender difference, while others explicitly encode it in articles or in morphemes 16 .”