End-to-End Bias Mitigation by Modelling Biases in Corpora
This addresses the issue of bias reliance in NLU models for researchers and practitioners, offering incremental improvements in debiasing methods.
The paper tackles the problem of natural language understanding models relying on dataset biases, which harms generalization to out-of-domain data, by proposing two learning strategies that use bias-only models to adjust loss and focus on hard examples, resulting in greatly improved robustness across natural language inference and fact verification benchmarks.
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. The biases are specified in terms of one or more bias-only models, which learn to leverage the dataset biases. During training, the bias-only models' predictions are used to adjust the loss of the base model to reduce its reliance on biases by down-weighting the biased examples and focusing the training on the hard examples. We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data. Results show that our debiasing methods greatly improve robustness in all settings and better transfer to other textual entailment datasets. Our code and data are publicly available in \url{https://github.com/rabeehk/robust-nli}.