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Register a new userFairness-aware Class Imbalanced Learning
التعلم الواضح من النزاهة
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الخلل من الطبقة هو تحد مشترك في العديد من مهام NLP، ولديه اتصالات واضحة إلى التحيز، في هذا التحيز في البيانات التدريبية يؤدي غالبا إلى دقة أعلى بالنسبة لمجموعات الأغلبية على حساب مجموعات الأقليات.ومع ذلك، كان هناك تقليديا قطع اتصال بين البحث في التعلم المتوازن في الفئة والتخفيف من التحيز، ولديه مؤخرا فقط تم النظر في اثنين من خلال عدسة مشتركة.في هذا العمل، نقيم أساليب التعلم الطويلة ذات الذيل الطويل لتغريد المعنويات وتصنيف الاحتلال، وتوسيع نهج قائم على الهامش مع طرق لفرض الإنصاف.نعرض تجريبيا من خلال تجارب محكومة أن الأساليب المقترحة تساعد في تخفيف كل من الخلل في الطبقة والتحيزات الديموغرافية.
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens. In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases.
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