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Recent years have seen a growing number of publications that analyse NaturalLanguage Inference (NLI) datasets for superficial cues, whether they underminethe complexity of the tasks underlying those datasets and how they impact thosemodels that are optimised and evaluated on this data. This structured surveyprovides an overview of the evolving research area by categorising reportedweaknesses in models and datasets and the methods proposed to reveal andalleviate those weaknesses for the English language. We summarise and discussthe findings and conclude with a set of recommendations for possible futureresearch directions. We hope it will be a useful resource for researchers whopropose new datasets, to have a set of tools to assess the suitability andquality of their data to evaluate various phenomena of interest, as well asthose who develop novel architectures, to further understand the implicationsof their improvements with respect to their model's acquired capabilities.

Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models