# Ensembling Graph Predictions for AMR Parsing

arXiv: https://arxiv.org/abs/2110.09131 · HTML: https://mlsanity.com/paper/2110.09131/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient One-Pass End-to-End Entity Linking for Questions](https://mlsanity.com/paper/2010.02413/) (2020)
- [Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://mlsanity.com/paper/1704.08381/) (2017)
- [AMR Parsing via Graph-Sequence Iterative Inference](https://mlsanity.com/paper/2004.05572/) (2020)
- [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016)
- [Improving AMR Parsing with Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2010.01771/) (2020)
- [AMR Parsing with Action-Pointer Transformer](https://mlsanity.com/paper/2104.14674/) (2021)
- [Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations](https://mlsanity.com/paper/1705.09980/) (2017)
- [Pushing the Limits of AMR Parsing with Self-Learning](https://mlsanity.com/paper/2010.10673/) (2020)
- [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)
- [Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser](https://mlsanity.com/paper/1609.07561/) (2016)
- [I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning](https://mlsanity.com/paper/2011.00766/) (2020)

## Built on by (most cited first, 15 of 15)

- [It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk](https://mlsanity.com/paper/2310.01387/) (2023)
- [DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations](https://mlsanity.com/paper/2203.09711/) (2022)
- [SMATCH++: Standardized and Extended Evaluation of Semantic Graphs](https://mlsanity.com/paper/2305.06993/) (2023)
- [Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation](https://mlsanity.com/paper/2405.03085/) (2024)
- [Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering](https://mlsanity.com/paper/2206.08486/) (2022)
- [Incorporating Graph Information in Transformer-based AMR Parsing](https://mlsanity.com/paper/2306.13467/) (2023)
- [ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs](https://mlsanity.com/paper/2204.08875/) (2022)
- [On the Role of Summary Content Units in Text Summarization Evaluation](https://mlsanity.com/paper/2404.01701/) (2024)
- [AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing](https://mlsanity.com/paper/2306.10786/) (2023)
- [Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification](https://mlsanity.com/paper/2301.11459/) (2023)
- [Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature Extraction Techniques, Ensembling, and Deep Learning Models](https://mlsanity.com/paper/2308.02022/) (2023)
- [Adapting Abstract Meaning Representation Parsing to the Clinical Narrative -- the SPRING THYME parser](https://mlsanity.com/paper/2405.09153/) (2024)
- [SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance](https://mlsanity.com/paper/2203.13226/) (2022)
- [Envisioning a Human-AI collaborative system to transform policies into decision models](https://mlsanity.com/paper/2212.06882/) (2022)
- [Evaluation of Finetuned LLMs in AMR Parsing](https://mlsanity.com/paper/2508.05028/) (2025)

## Abstract (arXiv)

In many machine learning tasks, models are trained to predict structure data such as graphs. For example, in natural language processing, it is very common to parse texts into dependency trees or abstract meaning representation (AMR) graphs. On the other hand, ensemble methods combine predictions from multiple models to create a new one that is more robust and accurate than individual predictions. In the literature, there are many ensembling techniques proposed for classification or regression problems, however, ensemble graph prediction has not been studied thoroughly. In this work, we formalize this problem as mining the largest graph that is the most supported by a collection of graph predictions. As the problem is NP-Hard, we propose an efficient heuristic algorithm to approximate the optimal solution. To validate our approach, we carried out experiments in AMR parsing problems. The experimental results demonstrate that the proposed approach can combine the strength of state-of-the-art AMR parsers to create new predictions that are more accurate than any individual models in five standard benchmark datasets.