# Core Semantic First: A Top-down Approach for AMR Parsing

arXiv: https://arxiv.org/abs/1909.04303 · HTML: https://mlsanity.com/paper/1909.04303/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 24 of 24)

- [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)
- [End-to-end Semantic Role Labeling with Neural Transition-based Model](https://mlsanity.com/paper/2101.00394/) (2021)
- [SMATCH++: Standardized and Extended Evaluation of Semantic Graphs](https://mlsanity.com/paper/2305.06993/) (2023)
- [Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity](https://mlsanity.com/paper/2108.11949/) (2021)
- [A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction](https://mlsanity.com/paper/2205.00241/) (2022)
- [Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04565/) (2021)
- [World Models for Math Story Problems](https://mlsanity.com/paper/2306.04347/) (2023)
- [Hierarchical Curriculum Learning for AMR Parsing](https://mlsanity.com/paper/2110.07855/) (2021)
- [Interpretable Proof Generation via Iterative Backward Reasoning](https://mlsanity.com/paper/2205.10714/) (2022)
- [Multilingual AMR Parsing with Noisy Knowledge Distillation](https://mlsanity.com/paper/2109.15196/) (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)
- [Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing](https://mlsanity.com/paper/2210.01425/) (2022)
- [Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation](https://mlsanity.com/paper/2210.09773/) (2022)
- [Levi Graph AMR Parser using Heterogeneous Attention](https://mlsanity.com/paper/2107.04152/) (2021)
- [Scene Graph Modification as Incremental Structure Expanding](https://mlsanity.com/paper/2209.09093/) (2022)
- [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)
- [AMR Parsing with Causal Hierarchical Attention and Pointers](https://mlsanity.com/paper/2310.11964/) (2023)
- [Adapting Abstract Meaning Representation Parsing to the Clinical Narrative -- the SPRING THYME parser](https://mlsanity.com/paper/2405.09153/) (2024)
- [Strictly Breadth-First AMR Parsing](https://mlsanity.com/paper/2211.03922/) (2022)
- [A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing](https://mlsanity.com/paper/2306.12018/) (2023)
- [Mechanistic Decomposition of Sentence Representations](https://mlsanity.com/paper/2506.04373/) (2025)

## Abstract (arXiv)

We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The textitcore semantic first principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.