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，但形式化与推理这些规范对人工智能系统而言仍是一项重大挑战。我们提出了一种新颖系统，用于从Social Chemistry 101数据集中提取自然语言形式的社会经验法则（ROTs），并将其转换为一阶逻辑，通过神经符号定理证明器进行推理。我们通过多个步骤实现这一目标：首先，将ROTs转换为抽象意义表示（AMR）——一种句子概念的图形化表征，并将AMR与RoBERTa嵌入对齐。随后，通过一种新颖算法生成AMR的替代简化版本，通过重新组合与合并嵌入来增强对不同文本表述及错误AMR解析的鲁棒性。接着将AMR转换为一阶逻辑，并使用神经符号定理证明器进行查询。本文旨在开发并评估一种以逻辑形式对社会情境进行显式推理的神经符号方法。