# A Graph-Based Neural Model for End-to-End Frame Semantic Parsing

arXiv: https://arxiv.org/abs/2109.12319 · HTML: https://mlsanity.com/paper/2109.12319/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [AMR Parsing as Sequence-to-Graph Transduction](https://mlsanity.com/paper/1905.08704/) (2019)
- [AMR Parsing as Graph Prediction with Latent Alignment](https://mlsanity.com/paper/1805.05286/) (2018)
- [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://mlsanity.com/paper/1805.04787/) (2018)
- [Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold](https://mlsanity.com/paper/1706.09528/) (2017)
- [Broad-Coverage Semantic Parsing as Transduction](https://mlsanity.com/paper/1909.02607/) (2019)
- [Syntactic Scaffolds for Semantic Structures](https://mlsanity.com/paper/1808.10485/) (2018)
- [Dependency or Span, End-to-End Uniform Semantic Role Labeling](https://mlsanity.com/paper/1901.05280/) (2019)
- [A Full End-to-End Semantic Role Labeler, Syntax-agnostic Over Syntax-aware?](https://mlsanity.com/paper/1808.03815/) (2018)
- [Learning Joint Semantic Parsers from Disjoint Data](https://mlsanity.com/paper/1804.05990/) (2018)
- [Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling](https://mlsanity.com/paper/1909.09814/) (2019)
- [Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks](https://mlsanity.com/paper/2011.13210/) (2020)
- [High-order Refining for End-to-end Chinese Semantic Role Labeling](https://mlsanity.com/paper/2009.06957/) (2020)

## Built on by (most cited first, 7 of 7)

- [Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures](https://mlsanity.com/paper/2212.01094/) (2022)
- [Can LLMs Extract Frame-Semantic Arguments?](https://mlsanity.com/paper/2502.12516/) (2025)
- [Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling](https://mlsanity.com/paper/2212.02036/) (2022)
- [Annotating FrameNet via Structure-Conditioned Language Generation](https://mlsanity.com/paper/2406.04834/) (2024)
- [Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models](https://mlsanity.com/paper/2306.10339/) (2023)
- [FrameNet Semantic Role Classification by Analogy](https://mlsanity.com/paper/2603.19825/) (2026)
- [The Multilingual FrameNet Corpus](https://mlsanity.com/paper/2608.23037/) (2026)

## Abstract (arXiv)

Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.