Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms
Open Access
- 1 January 2025
- conference paper
- Published by Association for Computational Linguistics (ACL)
- p. 32221-32237
Abstract
Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Maria Symeonaki, Giorgos Stamou. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.Keywords
All Related Versions
This publication has 32 references indexed in Scilit:
- A survey on LLM-as-a-judgeThe Innovation, 2026
- Gender bias in machine learning: insights from official labour statistics and textual analysisQuality & Quantity, 2025
- How to measure gender bias in machine translation: Real-world oriented machine translators, multiple reference pointsSocial Sciences & Humanities Open, 2022
- Gender Bias in Machine TranslationTransactions of the Association for Computational Linguistics, 2021
- COMET: A Neural Framework for MT Evaluation2020
- Language (Technology) is Power: A Critical Survey of “Bias” in NLP2020
- Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem2020
- Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus2020
- Scientific research must take gender into accountNature, 2014
- Generic masculine words and thinkingWomen's Studies International Quarterly, 1980