  Popular Natural Language Inference (NLI) datasets have been shown to be
tainted by hypothesis-only biases. Adversarial learning may help models ignore
sensitive biases and spurious correlations in data. We evaluate whether
adversarial learning can be used in NLI to encourage models to learn
representations free of hypothesis-only biases. Our analyses indicate that the
representations learned via adversarial learning may be less biased, with only
small drops in NLI accuracy.
