论文标题
使用自动推理工具探索自然语言推理任务
Probing the Natural Language Inference Task with Automated Reasoning Tools
论文作者
论文摘要
自然语言推论（NLI）任务是现代NLP的重要任务，因为它问一个广泛的问题，其中许多其他任务可能可降低：给定两次句子，第一个句子是否需要第二个？尽管NLI当前基准数据集的最先进是基于深度学习的，但值得使用其他技术来检查NLI任务的逻辑结构。我们通过测试一个可以使用机器导向的自然语言（Frigo Control的英语）来解析NLI句子的效果，以及自动定理掠夺者如何对产生的公式进行推理。为了提高性能，我们制定了一组句法和语义转换规则。我们报告其性能，并讨论对基于NLI和基于逻辑的NLP的影响。
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.
