mlsanityData to 7 Oct 2026

GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages

arXiv 2402.14277 ↗First posted 22 Feb 2024cs.CL cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studies on gender bias in translations into English from weakly gendered-languages, there are no benchmarks for evaluating this phenomenon or for assessing mitigation strategies. To address this gap, we introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus, that consists of human translations from Turkish, Hungarian, Finnish, and Persian into English. Each translation is accompanied by feminine, masculine, and neutral variants. The dataset, which contains between 1250 and 1850 instances for each of the four language pairs, features natural sentences with a wide range of sentence lengths and domains, challenging translation rewriters on various linguistic phenomena. Additionally, we present a translation gender rewriting solution built with GPT-4 and use GATE X-E to evaluate it. We open source our contributions to encourage further research on gender debiasing.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

18 of 39 references.

References · 18

  1. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  2. 2019Evaluating Gender Bias in Machine Translationcited by 247
  3. 2019WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipediacited by 221
  4. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matterscited by 221
  5. 2019CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairscited by 136
  6. 2018Assessing Gender Bias in Machine Translation -- A Case Study with Google Translatecited by 121
  7. 2020Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problemcited by 86
  8. 2020Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpuscited by 57
  9. 2020QED: A Framework and Dataset for Explanations in Question Answeringcited by 55
  10. 2021NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternativescited by 46
  11. 2021They, Them, Theirs: Rewriting with Gender-Neutral Englishcited by 44
  12. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
6 more
  1. 2021Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translationcited by 28
  2. 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translationcited by 23
  3. 2023Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scalecited by 23
  4. 2023Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challengescited by 22
  5. 2023GATE: A Challenge Set for Gender-Ambiguous Translation Examplescited by 18
  6. 2021Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Modelscited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Gender Bias in English-to-Greek Machine Translationcited by 3
  2. 2026Lightweight Fairness for LLM-Based Recommendations via Kernelized Projection and Gated Adapterscited by 1
  3. 2025Probing Gender Bias in Multilingual LLMs: A Case Study of Stereotypes in Persiancited by 0
  4. 2026Gender Bias in MT for a Genderless Language: New Benchmarks for Basquecited by 0