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State-of-the-art natural language processing (NLP) models often learn tomodel dataset biases and surface form correlations instead of features thattarget the intended underlying task. Previous work has demonstrated effectivemethods to circumvent these issues when knowledge of the bias is available. Weconsider cases where the bias issues may not be explicitly identified, and showa method for training models that learn to ignore these problematiccorrelations. Our approach relies on the observation that models with limitedcapacity primarily learn to exploit biases in the dataset. We can leverage theerrors of such limited capacity models to train a more robust model in aproduct of experts, thus bypassing the need to hand-craft a biased model. Weshow the effectiveness of this method to retain improvements inout-of-distribution settings even if no particular bias is targeted by thebiased model.

Learning from others' mistakes: Avoiding dataset biases without modeling them