  The prior work on natural language inference (NLI) debiasing mainly targets
at one or few known biases while not necessarily making the models more robust.
In this paper, we focus on the model-agnostic debiasing strategies and explore
how to (or is it possible to) make the NLI models robust to multiple distinct
adversarial attacks while keeping or even strengthening the models'
generalization power. We firstly benchmark prevailing neural NLI models
including pretrained ones on various adversarial datasets. We then try to
combat distinct known biases by modifying a mixture of experts (MoE) ensemble
method and show that it's nontrivial to mitigate multiple NLI biases at the
same time, and that model-level ensemble method outperforms MoE ensemble
method. We also perform data augmentation including text swap, word
substitution and paraphrase and prove its efficiency in combating various
(though not all) adversarial attacks at the same time. Finally, we investigate
several methods to merge heterogeneous training data (1.35M) and perform model
ensembling, which are straightforward but effective to strengthen NLI models.
