# Neuro-symbolic Commonsense Social Reasoning

arXiv: https://arxiv.org/abs/2303.08264 · HTML: https://mlsanity.com/paper/2303.08264/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018)
- [End-to-End Differentiable Proving](https://mlsanity.com/paper/1705.11040/) (2017)
- [Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences](https://mlsanity.com/paper/2012.15738/) (2020)
- [Leveraging Abstract Meaning Representation for Knowledge Base Question Answering](https://mlsanity.com/paper/2012.01707/) (2020)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Inducing and Using Alignments for Transition-based AMR Parsing](https://mlsanity.com/paper/2205.01464/) (2022)
- [Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations](https://mlsanity.com/paper/2011.13354/) (2020)

## Built on by (most cited first, 5 of 5)

- [Neuro-Symbolic AI in 2024: A Systematic Review](https://mlsanity.com/paper/2501.05435/) (2025)
- [Identification of Entailment and Contradiction Relations for Natural Language Argumentation: A Neurosymbolic Approach](https://mlsanity.com/paper/2405.01259/) (2024)
- [Understanding Enthymemes in Argument Maps: Bridging Argument Mining and Logic-based Argumentation](https://mlsanity.com/paper/2408.08648/) (2024)
- [Making Implicit Premises Explicit in Logical Understanding of Enthymemes](https://mlsanity.com/paper/2603.06114/) (2026)
- [Identifying Implicit Premises for Logical Reconstruction of Argument Graphs](https://mlsanity.com/paper/2608.18821/) (2026)

## Abstract (arXiv)

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.