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Found 6 papers, 2 papers with code
GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages
Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern.
Reducing Gender Bias in Machine Translation through Counterfactual Data Generation
By using this data to fine-tune an existing NMT model, they show that gender bias can be significantly mitigated, albeit at the expense of translation quality due to catastrophic forgetting.
Evaluating Gender Bias in the Translation of Gender-Neutral Languages into English
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.
GATE: A Challenge Set for Gender-Ambiguous Translation Examples
Although recent years have brought significant progress in improving translation of unambiguously gendered sentences, translation of ambiguously gendered input remains relatively unexplored.
Achieving Human Parity on Automatic Chinese to English News Translation
Machine translation has made rapid advances in recent years.
Ranked #3 on
Machine Translation
on WMT 2017 English-Chinese
