# AMR Parsing with Action-Pointer Transformer

arXiv: https://arxiv.org/abs/2104.14674 · HTML: https://mlsanity.com/paper/2104.14674/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 52 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016)
- [Learning Deep Generative Models of Graphs](https://mlsanity.com/paper/1803.03324/) (2018)
- [AMR Parsing as Sequence-to-Graph Transduction](https://mlsanity.com/paper/1905.08704/) (2019)
- [AMR Parsing via Graph-Sequence Iterative Inference](https://mlsanity.com/paper/2004.05572/) (2020)
- [Simpler but More Accurate Semantic Dependency Parsing](https://mlsanity.com/paper/1807.01396/) (2018)
- [Transition-based Parsing with Stack-Transformers](https://mlsanity.com/paper/2010.10669/) (2020)
- [Stack-Pointer Networks for Dependency Parsing](https://mlsanity.com/paper/1805.01087/) (2018)
- [AMR Parsing as Graph Prediction with Latent Alignment](https://mlsanity.com/paper/1805.05286/) (2018)
- [Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://mlsanity.com/paper/1909.00136/) (2019)
- [VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification](https://mlsanity.com/paper/2004.05707/) (2020)
- [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016)
- [Broad-Coverage Semantic Parsing as Transduction](https://mlsanity.com/paper/1909.02607/) (2019)
- [Improving AMR Parsing with Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2010.01771/) (2020)
- [Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning](https://mlsanity.com/paper/1905.13370/) (2019)
- [Core Semantic First: A Top-down Approach for AMR Parsing](https://mlsanity.com/paper/1909.04303/) (2019)
- [Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations](https://mlsanity.com/paper/1705.09980/) (2017)
- [Simple Unsupervised Summarization by Contextual Matching](https://mlsanity.com/paper/1907.13337/) (2019)
- [AMR Dependency Parsing with a Typed Semantic Algebra](https://mlsanity.com/paper/1805.11465/) (2018)
- [Transition-based Semantic Dependency Parsing with Pointer Networks](https://mlsanity.com/paper/2005.13344/) (2020)
- [Pushing the Limits of AMR Parsing with Self-Learning](https://mlsanity.com/paper/2010.10673/) (2020)
- [AMR Parsing using Stack-LSTMs](https://mlsanity.com/paper/1707.07755/) (2017)
- [Compositional Semantic Parsing Across Graphbanks](https://mlsanity.com/paper/1906.11746/) (2019)
- [Robust Incremental Neural Semantic Graph Parsing](https://mlsanity.com/paper/1704.07092/) (2017)
- [Encoder-Decoder Shift-Reduce Syntactic Parsing](https://mlsanity.com/paper/1706.07905/) (2017)

## Built on by (most cited first, 25 of 25)

- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing](https://mlsanity.com/paper/2110.15534/) (2021)
- [DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations](https://mlsanity.com/paper/2203.09711/) (2022)
- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [Ensembling Graph Predictions for AMR Parsing](https://mlsanity.com/paper/2110.09131/) (2021)
- [Inducing and Using Alignments for Transition-based AMR Parsing](https://mlsanity.com/paper/2205.01464/) (2022)
- [Hierarchical Curriculum Learning for AMR Parsing](https://mlsanity.com/paper/2110.07855/) (2021)
- [Compositional Task-Oriented Parsing as Abstractive Question Answering](https://mlsanity.com/paper/2205.02068/) (2022)
- [SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases](https://mlsanity.com/paper/2109.13430/) (2021)
- [CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction](https://mlsanity.com/paper/2205.00498/) (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)
- [Learning to Transpile AMR into SPARQL](https://mlsanity.com/paper/2112.07877/) (2021)
- [Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning](https://mlsanity.com/paper/2307.02689/) (2023)
- [SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation](https://mlsanity.com/paper/2210.08675/) (2022)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)
- [Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing](https://mlsanity.com/paper/2305.04346/) (2023)
- [Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledge](https://mlsanity.com/paper/2412.10207/) (2024)
- [Survey of Abstract Meaning Representation: Then, Now, Future](https://mlsanity.com/paper/2505.03229/) (2025)
- [Concept than Document: Context Compression via AMR-based Conceptual Entropy](https://mlsanity.com/paper/2511.18832/) (2025)
- [AMR Parsing with Causal Hierarchical Attention and Pointers](https://mlsanity.com/paper/2310.11964/) (2023)
- [Guiding AMR Parsing with Reverse Graph Linearization](https://mlsanity.com/paper/2310.08860/) (2023)
- [Modeling Unified Semantic Discourse Structure for High-quality Headline Generation](https://mlsanity.com/paper/2403.15776/) (2024)
- [Evaluation of Finetuned LLMs in AMR Parsing](https://mlsanity.com/paper/2508.05028/) (2025)

## Abstract (arXiv)

Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived. Transition-based parsers operate over the sentence from left to right, capturing this inductive bias via alignments at the cost of limited expressiveness. In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. We model the transitions as well as the pointer mechanism through straightforward modifications within a single Transformer architecture. Parser state and graph structure information are efficiently encoded using attention heads. We show that our action-pointer approach leads to increased expressiveness and attains large gains (+1.6 points) against the best transition-based AMR parser in very similar conditions. While using no graph re-categorization, our single model yields the second best Smatch score on AMR 2.0 (81.8), which is further improved to 83.4 with silver data and ensemble decoding.