# AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing

arXiv: https://arxiv.org/abs/2306.10786 · HTML: https://mlsanity.com/paper/2306.10786/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://mlsanity.com/paper/2112.07916/) (2021)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [Leveraging Abstract Meaning Representation for Knowledge Base Question Answering](https://mlsanity.com/paper/2012.01707/) (2020)
- [Semantic Neural Machine Translation using AMR](https://mlsanity.com/paper/1902.07282/) (2019)
- [Abstract Meaning Representation for Multi-Document Summarization](https://mlsanity.com/paper/1806.05655/) (2018)
- [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation](https://mlsanity.com/paper/1808.09160/) (2018)
- [AMR Similarity Metrics from Principles](https://mlsanity.com/paper/2001.10929/) (2020)
- [Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity](https://mlsanity.com/paper/2108.11949/) (2021)
- [Ensembling Graph Predictions for AMR Parsing](https://mlsanity.com/paper/2110.09131/) (2021)
- [RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy](https://mlsanity.com/paper/1604.01278/) (2016)
- [I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning](https://mlsanity.com/paper/2011.00766/) (2020)
- [Better Smatch = Better Parser? AMR evaluation is not so simple anymore](https://mlsanity.com/paper/2210.06461/) (2022)
- [ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs](https://mlsanity.com/paper/2204.08875/) (2022)

## Built on by (most cited first, 3 of 3)

- [It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk](https://mlsanity.com/paper/2310.01387/) (2023)
- [Improving Minimum Bayes Risk Decoding with Multi-Prompt](https://mlsanity.com/paper/2407.15343/) (2024)
- [CoKe: Customizable Fine-Grained Story Evaluation via Chain-of-Keyword Rationalization](https://mlsanity.com/paper/2503.17136/) (2025)

## Abstract (arXiv)

In this paper, we examine the current state-of-the-art in AMR parsing, which relies on ensemble strategies by merging multiple graph predictions. Our analysis reveals that the present models often violate AMR structural constraints. To address this issue, we develop a validation method, and show how ensemble models can exploit SMATCH metric weaknesses to obtain higher scores, but sometimes result in corrupted graphs. Additionally, we highlight the demanding need to compute the SMATCH score among all possible predictions. To overcome these challenges, we propose two novel ensemble strategies based on Transformer models, improving robustness to structural constraints, while also reducing the computational time. Our methods provide new insights for enhancing AMR parsers and metrics. Our code is available at hrefhttps://www.github.com/babelscape/AMRs-Assemblegithub.com/babelscape/AMRs-Assemble.