# An Incremental Parser for Abstract Meaning Representation

arXiv: https://arxiv.org/abs/1608.06111 · HTML: https://mlsanity.com/paper/1608.06111/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2016.

## Built on by (most cited first, 30 of 34)

- [SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features](https://mlsanity.com/paper/2206.07023/) (2022)
- [AMR Parsing with Action-Pointer Transformer](https://mlsanity.com/paper/2104.14674/) (2021)
- [Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing](https://mlsanity.com/paper/2110.15534/) (2021)
- [SMATCH++: Standardized and Extended Evaluation of Semantic Graphs](https://mlsanity.com/paper/2305.06993/) (2023)
- [Process-Level Representation of Scientific Protocols with Interactive Annotation](https://mlsanity.com/paper/2101.10244/) (2021)
- [Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04565/) (2021)
- [Ensembling Graph Predictions for AMR Parsing](https://mlsanity.com/paper/2110.09131/) (2021)
- [DocAMR: Multi-Sentence AMR Representation and Evaluation](https://mlsanity.com/paper/2112.08513/) (2021)
- [ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation](https://mlsanity.com/paper/2305.16585/) (2023)
- [Hierarchical Curriculum Learning for AMR Parsing](https://mlsanity.com/paper/2110.07855/) (2021)
- [Incorporating Graph Information in Transformer-based AMR Parsing](https://mlsanity.com/paper/2306.13467/) (2023)
- [Grounded Graph Decoding Improves Compositional Generalization in Question Answering](https://mlsanity.com/paper/2111.03642/) (2021)
- [AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite](https://mlsanity.com/paper/2312.03480/) (2023)
- [Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations](https://mlsanity.com/paper/2404.12698/) (2024)
- [ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs](https://mlsanity.com/paper/2204.08875/) (2022)
- [One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets](https://mlsanity.com/paper/2106.04476/) (2021)
- [Discourse Representation Structure Parsing for Chinese](https://mlsanity.com/paper/2306.09725/) (2023)
- [AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing](https://mlsanity.com/paper/2306.10786/) (2023)
- [SR-LLM: Rethinking the Structured Representation in Large Language Model](https://mlsanity.com/paper/2502.14352/) (2025)
- [Levi Graph AMR Parser using Heterogeneous Attention](https://mlsanity.com/paper/2107.04152/) (2021)
- [StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations](https://mlsanity.com/paper/2109.09853/) (2021)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)
- [Understanding Enthymemes in Argument Maps: Bridging Argument Mining and Logic-based Argumentation](https://mlsanity.com/paper/2408.08648/) (2024)
- [Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models](https://mlsanity.com/paper/2501.00953/) (2025)
- [Survey of Abstract Meaning Representation: Then, Now, Future](https://mlsanity.com/paper/2505.03229/) (2025)
- [A Transition-based Parser for Unscoped Episodic Logical Forms](https://mlsanity.com/paper/2103.08759/) (2021)
- [Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04814/) (2021)
- [AMR Parsing with Causal Hierarchical Attention and Pointers](https://mlsanity.com/paper/2310.11964/) (2023)
- [Estimating Text Similarity based on Semantic Concept Embeddings](https://mlsanity.com/paper/2401.04422/) (2024)
- [Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings](https://mlsanity.com/paper/2206.06229/) (2022)

## Abstract (arXiv)

Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.