Automatically Identifying Gender Issues in Machine Translation using Perturbations

By Hila Gonen, Kellie Webster. 2020

46 citations recorded by the source corpus.

The successful application of neural methods to machine translation has realized huge quality advances for the community. With these improvements, many have noted outstanding challenges, including the modeling and treatment of gendered language. While previous studies have identified issues using synthetic examples, we develop a novel technique to mine examples from real world data to explore challenges for deployed systems. We use our method to compile an evaluation benchmark spanning examples for four languages from three language families, which we publicly release to facilitate research. The examples in our benchmark expose where model representations are gendered, and the unintended consequences these gendered representations can have in downstream application.

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

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “…Sedoc and Ungar (2019); Stanovsky et al. (2019); Sweeney and Najafian (2019); Vaidya et al. (2019); Zhiltsova et al. (2019); Chopra et al. (2020); Gonen and Webster (2020); Gyamfi et al. (2020); Hube et al. (2020); Ravfogel et al. (2020); Rios (2020); Ross et al. (2020); Saunders and Byrne…”
Societal Biases in Language Generation: Progress and Challenges (2021, background): “, enfermera or enfermero in Spanish, gendered pronouns) (Cho et al., 2019; Stanovsky et al., 2019; Gonen and Webster, 2020; Kocmi et al., 2020).”
Investigating Failures of Automatic Translation in the Case of Unambiguous Gender (2021, methodology): “Recently, several works (Stanovsky et al., 2019; Prates et al., 2019; Gonen and Webster, 2020; González et al., 2020) investigated gender bias in”
Evaluation Gaps in Machine Learning Practice (2022, background): “Input sensitivity testing can provide useful information about the sensitivity of the classifier to dimensions of input variation known to be of concern (e.g., gender in text [21, 66, 80, 180]).”
Theories of “Gender” in NLP Bias Research (2022, unknown): “03862 [57] Hila Gonen and Kellie Webster.”
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023, background): “, 2022) are the cause of this impact, and some studies have investigated the connection between training data and downstream task model behavior (Gonen and Webster, 2020; Li et al., 2020; Dodge et al., 2021).”
Gender-Aware Reinflection using Linguistically Enhanced Neural Models (2020, background): “…et al., 2017; Vanmassenhove et al., 2018; Font and Costa-jussà, 2019; Moryossef et al., 2019; Stanovsky et al., 2019; Stafanoviˇcs et al., 2020; Gonen and Webster, 2020), named entity recognition (Mehrabi et al., 2019), dialogue systems (Dinan et al., 2019), and language modeling (Lu et al.,…”
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics (2021, background): “…demographics (Dixon et al., 2018; Zhao et al., 2018; Rudinger et al., 2018; Garg et al., 2019; Borkan et al., 2019; Stanovsky et al., 2019; Gonen and Webster, 2020; Huang et al., 2020a; Nangia et al., 2020), which can reportedly cause a variety of serious harms, like unfair allocation of…”
Gender Bias in Machine Translation (2021, background): “(Gonen and Webster, 2020) BERT-based perturbations on natural sentences b R: under-rep, stereotyping”