  Recent years have seen a growing number of publications that analyse Natural
Language Inference (NLI) datasets for superficial cues, whether they undermine
the complexity of the tasks underlying those datasets and how they impact those
models that are optimised and evaluated on this data. This structured survey
provides an overview of the evolving research area by categorising reported
weaknesses in models and datasets and the methods proposed to reveal and
alleviate those weaknesses for the English language. We summarise and discuss
the findings and conclude with a set of recommendations for possible future
research directions. We hope it will be a useful resource for researchers who
propose new datasets, to have a set of tools to assess the suitability and
quality of their data to evaluate various phenomena of interest, as well as
those who develop novel architectures, to further understand the implications
of their improvements with respect to their model's acquired capabilities.
