In this position paper, we propose a way of exploiting formal proofs to put
forward several explainable natural language inference (NLI) tasks. The formal
proofs will be produced by a reliable and high-performing logic-based NLI
system. Taking advantage of the in-depth information available in the generated
formal proofs, we show how it can be used to define NLI tasks with structured
explanations. The proposed tasks can be ordered according to difficulty defined
in terms of the granularity of explanations. We argue that the tasks will
suffer with substantially fewer shortcomings than the existing explainable NLI
tasks (or datasets).

我们提出利用形式证明来推进几个可解释的自然语言推理（NLI）任务的方法，并利用可靠和高性能的基于逻辑的 NLI 系统生成形式证明。通过利用生成的形式证明中的深度信息，我们展示了如何使用它来定义具有结构化解释的 NLI 任务。所提出的任务可以根据解释的粒度难度进行排序，并且我们认为这些任务在许多方面都比现有的可解释 NLI 任务（或数据集）具有更少缺陷。