Social norms underlie all human social interactions, yet formalizing and reasoning with them remains a major challenge for AI systems. We present a novel system for taking social rules of thumb (ROTs) in natural language from the Social Chemistry 101 dataset and converting them to first-order logic where reasoning is performed using a neuro-symbolic theorem prover. We accomplish this in several steps. First, ROTs are converted into Abstract Meaning Representation (AMR), which is a graphical representation of the concepts in a sentence, and align the AMR with RoBERTa embeddings. We then generate alternate simplified versions of the AMR via a novel algorithm, recombining and merging embeddings for added robustness against different wordings of text, and incorrect AMR parses. The AMR is then converted into first-order logic, and is queried with a neuro-symbolic theorem prover. The goal of this paper is to develop and evaluate a neuro-symbolic method which performs explicit reasoning about social situations in a logical form.
翻译:社会规范是人类所有社会互动的基础,然而,正规化和与这些规范的推理仍然是AI系统的一大挑战。我们从社会化学 101 数据集中提出了一个以自然语言采用自然语言的拇指社会规则的新体系,并将其转换为一阶逻辑,即使用神经-共性理论验证法进行推理。我们通过几个步骤实现了这一点。首先,ROT转换为抽象代表法(AMR),这是句子中概念的图形表达法,使AMR与RoBERTA嵌入法相匹配。然后,我们通过一种小的算法,重组和合并嵌入,以针对不同的文字措辞添加稳健性,以及不正确的AMR等,产生替代简化的AMR。然后,AMR被转换为一阶逻辑,用神经-共性理论验证法进行查询。本文的目的是制定和评估一种神经-共性方法,以逻辑形式对社会状况进行明确的推理。</s>