Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function

Gender bias exists in natural language datasets which neural language models tend to learn, resulting in biased text generation. In this research, we propose a debiasing approach based on the loss function modification. We introduce a new term to the loss function which attempts to equalize the prob...

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Hauptverfasser:	Qian, Yusu, Muaz, Urwa, Zhang, Ben, Hyun, Jae Won
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computation and Language
Online-Zugang:	Volltext bestellen
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