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Existing Natural Language Inference (NLI) datasets, while being instrumentalin the advancement of Natural Language Understanding (NLU) research, are notrelated to scientific text. In this paper, we introduce SciNLI, a large datasetfor NLI that captures the formality in scientific text and contains 107,412sentence pairs extracted from scholarly papers on NLP and computationallinguistics. Given that the text used in scientific literature differs vastlyfrom the text used in everyday language both in terms of vocabulary andsentence structure, our dataset is well suited to serve as a benchmark for theevaluation of scientific NLU models. Our experiments show that SciNLI is harderto classify than the existing NLI datasets. Our best performing model withXLNet achieves a Macro F1 score of only 78.18% and an accuracy of 78.23%showing that there is substantial room for improvement.

SciNLI: A Corpus for Natural Language Inference on Scientific Text