Gender Bias in Machine Translation

By Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi. 2021

263 citations recorded by the source corpus.

Abstract Machine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, studies of gender bias in MT still lack cohesion. This advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.

Subfields: Artificial Intelligence, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models (2021, background): “, 2021), neural machine translation (Cho et al., 2019; Stanovsky et al., 2019; Prates et al., 2020; Renduchintala et al., 2021; Savoldi et al., 2021), or language modeling (Nangia et al.”
A Survey on Gender Bias in Natural Language Processing (2021, methodology): “Tan and Celis [2019]; Zhao et al. [2019] examine datasets that were used as training corpora for the popular NLP methods and find that the occurrence of male pronouns is consistently higher across all datasets and evidence of stereotypical associations.”
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics (2021, background): “There are also many task speciﬁc surveys, e.g., for language generation (Sheng et al., 2021) or machine translation (Savoldi et al., 2021).”
Theories of “Gender” in NLP Bias Research (2022, background): “[125] Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, and Marco Turchi.”
Evaluating Gender Bias in Hindi-English Machine Translation (2021, methodology): “There has been a recent increase in the studies on gender bias in natural language processing considering bias in word embeddings, bias ampliﬁcation, and methods to evaluate bias (Savoldi et al., 2021), with some evaluation methods introduced primarily to measure gender bias in MT systems.”