Evaluating Gender Bias in Machine Translation

By Gabriel Stanovsky, Noah A. Smith, Luke S. Zettlemoyer. 2019

471 citations recorded by the source corpus.

We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., “The doctor asked the nurse to help her in the operation”). We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word “doctor”). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages. Our data and code are publicly available at https://github.com/gabrielStanovsky/mt_gender.

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

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Citations in the corpus

PaLM: Scaling Language Modeling with Pathways (2022, background): “Coreference resolution is an important linguistic capability for language systems such as question answering, summarization, and translation (Stanovsky et al., 2019; Webster & Pitler, 2020).”
Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “…and Glavaš (2019); Lee et al. (2019); Manzini et al. (2019); Mayfield et al. (2019); Précenth (2019); Pujari et al. (2019); Ruane et al. (2019); Stanovsky et al. (2019); Sun et al. (2019); Tan and Celis (2019); Webster et al. (2019); Zmigrod et al. (2019); Gyamfi et al. (2020); Hube et al.…”
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021, background): “Coreference resolution is a capability that many applications require to perform well, including machine translation (Stanovsky et al., 2019; Webster & Pitler, 2020) and question answering (Lamm et al., 2020).”
Societal Biases in Language Generation: Progress and Challenges (2021, methodology): “While Google Translate 3 has been the most popular commercial system to analyze for gender biases (Prates et al., 2019; Moryossef et al., 2019; Stanovsky et al., 2019; Cho et al., 2019; Farkas and N´emeth, 2020), Stanovsky et al. (2019) 2021) also examine Yandex.”
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias (2022, background): “For example, several works studied the bias in models using downstream tasks such as coreference resolution (Rudinger et al., 2018; Zhao et al., 2018), natural language inference (NLI) (Poliak et al., 2018; Sharma et al., 2021) and machine translation (Stanovsky et al., 2019).”
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.”
Multi-Dimensional Gender Bias Classification (2020, neutral): “, 2019), and machine translation (Stanovsky et al., 2019; Hovy et al., 2020).”
Towards Controllable Biases in Language Generation (2020, background): “s metrics (Sheng et al., 2019; Bordia and Bowman,2019),createdatasets forthe study of biases (Sheng et al., 2019; Nadeem et al., 2020), and analyze biases in machine translation (Prates et al., 2019; Stanovsky et al., 2019). The major challenge of analyzing and mitigating biases in NLG is having a w”
A Distributional Approach to Controlled Text Generation (2020, background): “There has been a large body of work analysing these distributional biases (Blodgett et al., 2020; Stanovsky et al., 2019; Prates et al., 2020; Sheng et al., 2019a; Brown et al., 2020b).”
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2019, background): “Based on the ﬁrst two corpora, Stanovsky et al. (2019) introduced a bias evaluation for machine translation, showing that most systems overamplify gender bias (see also Prates et al. (2018)).”
UNQOVERing Stereotypical Biases via Underspecified Questions (2020, background): “, 2019), machine translation (Stanovsky et al., 2019; Cho et al., 2019), textual entailment (Dev et al.”
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings (2022, background): “WinoMT (Stanovsky et al., 2019) is one of the few benchmarks that covers multiple languages, but it comes with its own downsides.”
Measuring and Reducing Gendered Correlations in Pre-trained Models (2020, neutral): “…et al., 2019; Romanov et al., 2019), sentiment analysis (Kiritchenko and Mohammad, 2018), coreference resolution (Rudinger et al., 2018; Zhao et al., 2018a; Webster et al., 2018), image captioning (Zhao et al., 2017), and machine translation (Stanovsky et al., 2019; Prates et al., 2018).”
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem (2020, positive): “This follows much recent work on NLP gender bias (Rudinger et al., 2018; Zhao et al., 2018; Zmigrod et al., 2019) including the release of WinoMT, a relevant challenge set for NMT (Stanovsky et al., 2019).”
Investigating Failures of Automatic Translation in the Case of Unambiguous Gender (2021, methodology): “Following Stanovsky et al. (2019), we extract token-alignments between the source occupation noun token and its translation in the target side.”
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021, background): “…2018; Devlin et al., 2019) have recently been shown to encode and amplify a range of stereo-typical human biases (e.g., gender or racial biases) (Zhao et al., 2019; Basta et al., 2019; Liang et al., 2020a,b), much like their static embedding pre-decessors (Bolukbasi et al., 2016; Caliskan et…”
MEGA: Multilingual Evaluation of Generative AI (2023, methodology): “We follow the method as reported by (Stanovsky et al., 2019) in their paper.”