  Many recent studies have shown that for models trained on datasets for
natural language inference (NLI), it is possible to make correct predictions by
merely looking at the hypothesis while completely ignoring the premise. In this
work, we manage to derive adversarial examples in terms of the hypothesis-only
bias and explore eligible ways to mitigate such bias. Specifically, we extract
various phrases from the hypotheses (artificial patterns) in the training sets,
and show that they have been strong indicators to the specific labels. We then
figure out `hard' and `easy' instances from the original test sets whose labels
are opposite to or consistent with those indications. We also set up baselines
including both pretrained models (BERT, RoBERTa, XLNet) and competitive
non-pretrained models (InferSent, DAM, ESIM). Apart from the benchmark and
baselines, we also investigate two debiasing approaches which exploit the
artificial pattern modeling to mitigate such hypothesis-only bias:
down-sampling and adversarial training. We believe those methods can be treated
as competitive baselines in NLI debiasing tasks.
