# Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

arXiv: https://arxiv.org/abs/1908.05957 · HTML: https://mlsanity.com/paper/1908.05957/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 29 of 29)

- [Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering](https://mlsanity.com/paper/2309.11206/) (2023)
- [Structural Adapters in Pretrained Language Models for AMR-to-text Generation](https://mlsanity.com/paper/2103.09120/) (2021)
- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Structural Information Preserving for Graph-to-Text Generation](https://mlsanity.com/paper/2102.06749/) (2021)
- [GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2204.06674/) (2022)
- [Unifying Structured Data as Graph for Data-to-Text Pre-Training](https://mlsanity.com/paper/2401.01183/) (2024)
- [Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning](https://mlsanity.com/paper/2501.01124/) (2025)
- [Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering](https://mlsanity.com/paper/2109.07095/) (2021)
- [Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals](https://mlsanity.com/paper/2307.00222/) (2023)
- [Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest](https://mlsanity.com/paper/2208.13472/) (2022)
- [Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation](https://mlsanity.com/paper/2210.09773/) (2022)
- [Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation](https://mlsanity.com/paper/2311.09467/) (2023)
- [Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification](https://mlsanity.com/paper/2106.15778/) (2021)
- [Oversmoothing Alleviation in Graph Neural Networks: A Survey and Unified View](https://mlsanity.com/paper/2405.01663/) (2024)
- [Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation](https://mlsanity.com/paper/2109.03808/) (2021)
- [How to Train Your Agent to Read and Write](https://mlsanity.com/paper/2101.00916/) (2021)
- [Levi Graph AMR Parser using Heterogeneous Attention](https://mlsanity.com/paper/2107.04152/) (2021)
- [Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph](https://mlsanity.com/paper/2112.00503/) (2021)
- [Graph-to-Text Generation with Dynamic Structure Pruning](https://mlsanity.com/paper/2209.07258/) (2022)
- [Scene Graph Modification as Incremental Structure Expanding](https://mlsanity.com/paper/2209.09093/) (2022)
- [Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters](https://mlsanity.com/paper/2302.05900/) (2023)
- [MGSA: Multi-Granularity Graph Structure Attention for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2409.10294/) (2024)
- [DISK: Domain-constrained Instance Sketch for Math Word Problem Generation](https://mlsanity.com/paper/2204.04686/) (2022)
- [Knowledge Graph for NLG in the context of conversational agents](https://mlsanity.com/paper/2307.01548/) (2023)
- [VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection](https://mlsanity.com/paper/2602.13880/) (2026)
- [A Survey : Neural Networks for AMR-to-Text](https://mlsanity.com/paper/2206.07328/) (2022)
- [Controlling Topic-Focus Articulation in Meaning-to-Text Generation using Graph Neural Networks](https://mlsanity.com/paper/2310.02053/) (2023)
- [Evaluating and Improving Graph to Text Generation with Large Language Models](https://mlsanity.com/paper/2501.14497/) (2025)
- [Forecasting West Nile virus with deep graph encoders](https://mlsanity.com/paper/2509.22657/) (2025)

## Abstract (arXiv)

We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Networks (DCGCNs). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMRto-text generation and syntax-based neural machine translation.