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GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies

arXiv 1912.04778 ↗First posted 10 Dec 2019cs.CLcited by 11

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Abstract (arXiv)

We introduce GeBioToolkit, a tool for extracting multilingual parallel corpora at sentence level, with document and gender information from Wikipedia biographies. Despite thegender inequalitiespresent in Wikipedia, the toolkit has been designed to extract corpus balanced in gender. While our toolkit is customizable to any number of languages (and different domains), in this work we present a corpus of 2,000 sentences in English, Spanish and Catalan, which has been post-edited by native speakers to become a high-quality dataset for machinetranslation evaluation. While GeBioCorpus aims at being one of the first non-synthetic gender-balanced test datasets, GeBioToolkit aims at paving the path to standardize procedures to produce gender-balanced datasets

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2019.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 4; 2024: 3.

11 papers.

  1. 2021Gender Bias in Machine Translationcited by 116
  2. 2021A Survey on Gender Bias in Natural Language Processingcited by 89
  3. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  4. 2021GENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomenacited by 16
  5. 2024What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Studycited by 15
  6. 2023Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoningcited by 7
  7. 2024GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanationscited by 5
  8. 2023Gender Inflected or Bias Inflicted: On Using Grammatical Gender Cues for Bias Evaluation in Machine Translationcited by 4
  9. 2023The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 Languagescited by 3
  10. 2024Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Reviewcited by 1
  11. 2023Don't Overlook the Grammatical Gender: Bias Evaluation for Hindi-English Machine Translationcited by 0