  Natural Language Inference (NLI) datasets contain annotation artefacts
resulting in spurious correlations between the natural language utterances and
their respective entailment classes. These artefacts are exploited by neural
networks even when only considering the hypothesis and ignoring the premise,
leading to unwanted biases. Belinkov et al. (2019b) proposed tackling this
problem via adversarial training, but this can lead to learned sentence
representations that still suffer from the same biases. We show that the bias
can be reduced in the sentence representations by using an ensemble of
adversaries, encouraging the model to jointly decrease the accuracy of these
different adversaries while fitting the data. This approach produces more
robust NLI models, outperforming previous de-biasing efforts when generalised
to 12 other datasets (Belinkov et al., 2019a; Mahabadi et al., 2020). In
addition, we find that the optimal number of adversarial classifiers depends on
the dimensionality of the sentence representations, with larger sentence
representations being more difficult to de-bias while benefiting from using a
greater number of adversaries.
