# Graph Neural Networks with Generated Parameters for Relation Extraction

arXiv: https://arxiv.org/abs/1902.00756 · HTML: https://mlsanity.com/paper/1902.00756/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2019.

## Built on by (most cited first, 27 of 27)

- [Interpreting and Unifying Graph Neural Networks with An Optimization Framework](https://mlsanity.com/paper/2101.11859/) (2021)
- [SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks](https://mlsanity.com/paper/2102.05034/) (2021)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Optimization-Induced Graph Implicit Nonlinear Diffusion](https://mlsanity.com/paper/2206.14418/) (2022)
- [Graph Based Network with Contextualized Representations of Turns in Dialogue](https://mlsanity.com/paper/2109.04008/) (2021)
- [Discriminative Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2106.01562/) (2021)
- [What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification](https://mlsanity.com/paper/2204.13516/) (2022)
- [Document-Level Relation Extraction with Sentences Importance Estimation and Focusing](https://mlsanity.com/paper/2204.12679/) (2022)
- [Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2105.09543/) (2021)
- [KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction](https://mlsanity.com/paper/2106.00459/) (2021)
- [Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation](https://mlsanity.com/paper/2112.04539/) (2021)
- [CorefDRE: Document-level Relation Extraction with coreference resolution](https://mlsanity.com/paper/2202.10744/) (2022)
- [Discovering the Representation Bottleneck of Graph Neural Networks](https://mlsanity.com/paper/2205.07266/) (2022)
- [ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction](https://mlsanity.com/paper/2309.01370/) (2023)
- [Anaphor Assisted Document-Level Relation Extraction](https://mlsanity.com/paper/2310.18604/) (2023)
- [GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction](https://mlsanity.com/paper/2205.03786/) (2022)
- [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022)
- [CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises](https://mlsanity.com/paper/2210.10581/) (2022)
- [Open Set Relation Extraction via Unknown-Aware Training](https://mlsanity.com/paper/2306.04950/) (2023)
- [Reward-based Input Construction for Cross-document Relation Extraction](https://mlsanity.com/paper/2405.20649/) (2024)
- [RE2: Region-Aware Relation Extraction from Visually Rich Documents](https://mlsanity.com/paper/2305.14590/) (2023)
- [Chinese Financial Text Emotion Mining: GCGTS -- A Character Relationship-based Approach for Simultaneous Aspect-Opinion Pair Extraction](https://mlsanity.com/paper/2308.02113/) (2023)
- [Document-Level Relation Extraction with Relation Correlation Enhancement](https://mlsanity.com/paper/2310.13000/) (2023)
- [Dependency Parsing is More Parameter-Efficient with Normalization](https://mlsanity.com/paper/2505.20215/) (2025)
- [Analyzing the Influence of Knowledge Graph Information on Relation Extraction](https://mlsanity.com/paper/2506.16343/) (2025)
- [Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey](https://mlsanity.com/paper/2607.09666/) (2026)
- [Configurable Semantic Chunking for Biomedical Information Extraction in Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.31139/) (2026)

## Abstract (arXiv)

Recently, progress has been made towards improving relational reasoning in machine learning field. Among existing models, graph neural networks (GNNs) is one of the most effective approaches for multi-hop relational reasoning. In fact, multi-hop relational reasoning is indispensable in many natural language processing tasks such as relation extraction. In this paper, we propose to generate the parameters of graph neural networks (GP-GNNs) according to natural language sentences, which enables GNNs to process relational reasoning on unstructured text inputs. We verify GP-GNNs in relation extraction from text. Experimental results on a human-annotated dataset and two distantly supervised datasets show that our model achieves significant improvements compared to baselines. We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.