Machine learning models, and in particular language models, are being applied to various tasks that require reasoning. While such models are good at capturing patterns their ability to reason in a trustable and controlled manner is frequently questioned. On the other hand, logic-based rule systems allow for controlled inspection and already established verification methods. However it is well-known that creating such systems manually is time-consuming and prone to errors. We explore the capability of transformers to translate sentences expressing rules in natural language into logical rules. We see reasoners as the most reliable tools for performing logical reasoning and focus on translating language into the format expected by such tools. We perform experiments using the DKET dataset from the literature and create a dataset for language to logic translation based on the Atomic knowledge bank.

翻译：机器学习模型，特别是语言模型，正被应用于各种需要推理的任务中。尽管这类模型在捕捉模式方面表现出色，但其以可信和可控方式进行推理的能力时常受到质疑。另一方面，基于逻辑的规则系统允许受控检查并已建立验证方法。然而，众所周知，手动创建此类系统既耗时又容易出错。我们探索了Transformer将表达规则的自然语言句子翻译成逻辑规则的能力。我们认为推理器是执行逻辑推理最可靠的工具，并专注于将语言翻译成这些工具所期望的格式。我们利用文献中的DKET数据集进行实验，并基于原子知识库创建了一个用于语言到逻辑翻译的数据集。