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Pre-trained neural language models give high performance on natural languageinference (NLI) tasks. But whether they actually understand the meaning of theprocessed sequences remains unclear. We propose a new diagnostics test suitewhich allows to assess whether a dataset constitutes a good testbed forevaluating the models' meaning understanding capabilities. We specificallyapply controlled corruption transformations to widely used benchmarks (MNLI andANLI), which involve removing entire word classes and often lead tonon-sensical sentence pairs. If model accuracy on the corrupted data remainshigh, then the dataset is likely to contain statistical biases and artefactsthat guide prediction. Inversely, a large decrease in model accuracy indicatesthat the original dataset provides a proper challenge to the models' reasoningcapabilities. Hence, our proposed controls can serve as a crash test fordeveloping high quality data for NLI tasks.

NLI Data Sanity Check: Assessing the Effect of Data Corruption on Model Performance