# I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning

arXiv: https://arxiv.org/abs/2011.00766 · HTML: https://mlsanity.com/paper/2011.00766/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Ensembling Graph Predictions for AMR Parsing](https://mlsanity.com/paper/2110.09131/) (2021)
- [Incorporating Graph Information in Transformer-based AMR Parsing](https://mlsanity.com/paper/2306.13467/) (2023)
- [AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing](https://mlsanity.com/paper/2306.10786/) (2023)
- [Large Language Models Are Also Good Prototypical Commonsense Reasoners](https://mlsanity.com/paper/2309.13165/) (2023)
- [Cross-lingual AMR Aligner: Paying Attention to Cross-Attention](https://mlsanity.com/paper/2206.07587/) (2022)
- [Abstract Meaning Representation for Hospital Discharge Summarization](https://mlsanity.com/paper/2506.14101/) (2025)
- [EventRR: Event Referential Reasoning for Referring Video Object Segmentation](https://mlsanity.com/paper/2508.07171/) (2025)
- [Graph Alignment Topology as an Inductive Bias for Grounding Detection](https://mlsanity.com/paper/2605.22963/) (2026)
- [Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation](https://mlsanity.com/paper/2609.13581/) (2026)

## Abstract (arXiv)

CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question. To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task. This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph. Moreover, ACP-based models are shown to outperform the baselines.