  Natural language inference (NLI) aims at predicting the relationship between
a given pair of premise and hypothesis. However, several works have found that
there widely exists a bias pattern called annotation artifacts in NLI datasets,
making it possible to identify the label only by looking at the hypothesis.
This irregularity makes the evaluation results over-estimated and affects
models' generalization ability. In this paper, we consider a more trust-worthy
setting, i.e., cross-dataset evaluation. We explore the impacts of annotation
artifacts in cross-dataset testing. Furthermore, we propose a training
framework to mitigate the impacts of the bias pattern. Experimental results
demonstrate that our methods can alleviate the negative effect of the artifacts
and improve the generalization ability of models.
