End-to-End Bias Mitigation by Modelling Biases in Corpora

ACL 2020 Rabeeh Karimi MahabadiYonatan BelinkovJames Henderson

Several recent studies have shown that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models that fail to generalize to out-of-domain datasets and are likely to perform poorly in real-world scenarios. We propose two learning strategies to train neural models, which are more robust to such biases and transfer better to out-of-domain datasets... (read more)

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