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 a pair of sentences, does the first entail the second? Although the state-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 of the NLI task. We do so by testing how well a machine-oriented controlled natural language (Attempto Controlled English) can be used to parse NLI sentences, and how well automated theorem provers can reason over the resulting formulae. To improve performance, we develop a set of syntactic and semantic transformation rules. We report their performance, and discuss implications for NLI and logic-based NLP.

中文翻译：

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使用自动推理工具探索自然语言推理任务

自然语言推理 (NLI) 任务是现代 NLP 中的一项重要任务，因为它提出了一个广泛的问题，许多其他任务可以简化为：给定一对句子，第一个是否包含第二个？尽管当前 NLI 基准数据集的最新技术是基于深度学习的，但值得使用其他技术来检查 NLI 任务的逻辑结构。我们通过测试面向机器的受控自然语言（Attempto Controlled English）如何很好地解析 NLI 句子，以及自动化定理证明者对结果公式的推理能力如何来做到这一点。为了提高性能，我们开发了一组句法和语义转换规则。我们报告了它们的性能，并讨论了对 NLI 和基于逻辑的 NLP 的影响。