# Compositional Semantic Parsing Across Graphbanks

arXiv: https://arxiv.org/abs/1906.11746 · HTML: https://mlsanity.com/paper/1906.11746/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

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

- [AMR Parsing with Action-Pointer Transformer](https://mlsanity.com/paper/2104.14674/) (2021)
- [DocAMR: Multi-Sentence AMR Representation and Evaluation](https://mlsanity.com/paper/2112.08513/) (2021)
- [Learning compositional structures for semantic graph parsing](https://mlsanity.com/paper/2106.04398/) (2021)
- [One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets](https://mlsanity.com/paper/2106.04476/) (2021)
- [Levi Graph AMR Parser using Heterogeneous Attention](https://mlsanity.com/paper/2107.04152/) (2021)
- [Compositional Generalization Requires Compositional Parsers](https://mlsanity.com/paper/2202.11937/) (2022)
- [Scope-enhanced Compositional Semantic Parsing for DRT](https://mlsanity.com/paper/2407.01899/) (2024)
- [The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task](https://mlsanity.com/paper/2107.01982/) (2021)
- [Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field Inference](https://mlsanity.com/paper/2204.03619/) (2022)
- [Polynomial Graph Parsing with Non-Structural Reentrancies](https://mlsanity.com/paper/2105.02033/) (2021)
- [A Higher-Order Semantic Dependency Parser](https://mlsanity.com/paper/2201.11312/) (2022)
- [Strictly Breadth-First AMR Parsing](https://mlsanity.com/paper/2211.03922/) (2022)
- [Rethinking Relation Classification with Graph Meaning Representations](https://mlsanity.com/paper/2310.09772/) (2023)
- [Structural Generalization on SLOG without Hand-Written Rules](https://mlsanity.com/paper/2604.26157/) (2026)
- [On the Role of Directionality in Structural Generalization](https://mlsanity.com/paper/2607.02307/) (2026)

## Abstract (arXiv)

Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.