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The Natural Language Inference (NLI) task is an important task in modern NLP,as it asks a broad question to which many other tasks may be reducible: Given apair of sentences, does the first entail the second? Although thestate-of-the-art on current benchmark datasets for NLI are deep learning-based,it is worthwhile to use other techniques to examine the logical structure ofthe NLI task. We do so by testing how well a machine-oriented controllednatural language (Attempto Controlled English) can be used to parse NLIsentences, and how well automated theorem provers can reason over the resultingformulae. To improve performance, we develop a set of syntactic and semantictransformation rules. We report their performance, and discuss implications forNLI and logic-based NLP.

Probing the Natural Language Inference Task with Automated Reasoning Tools