Publication: Reducing Gender Bias in NMT with FUDGE
Reducing Gender Bias in NMT with FUDGE
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Lu, T., Aepli, N., & Rios, A. (2023). Reducing Gender Bias in NMT with FUDGE (E. Vanmassenhove, B. Savoldi, L. Bentivogli, J. Daems, & J. Hackenbuchner, Eds.; pp. 61–69).
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Abstract
Gender bias appears in many neural machine translation (NMT) models and commercial translation software. Research has become more aware of this problem in recent years and there has been work on mitigating gender bias. However, the challenge of addressing gender bias in NMT persists. This work utilizes a controlled text generation method, Future Discriminators for Generation (FUDGE), to reduce the so-called Speaking As gender bias. This bias emerges when translating from English to a language that openly marks the gender of the speake
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Lu, T., Aepli, N., & Rios, A. (2023). Reducing Gender Bias in NMT with FUDGE (E. Vanmassenhove, B. Savoldi, L. Bentivogli, J. Daems, & J. Hackenbuchner, Eds.; pp. 61–69).
