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GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages
Feb. 23, 2024, 5:48 a.m. | Spencer Rarrick, Ranjita Naik, Sundar Poudel, Vishal Chowdhary
cs.CL updates on arXiv.org arxiv.org
Abstract: 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, …
abstract adoption arxiv benchmarks bias challenge cs.ai cs.cl english fair gate gender gender bias languages machine machine translation neural machine translation quality set studies translation type
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