# Dependency Parsing with Dilated Iterated Graph CNNs

arXiv: https://arxiv.org/abs/1705.00403 · HTML: https://mlsanity.com/paper/1705.00403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2017.

## Built on by (most cited first, 1 of 1)

- [Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement](https://mlsanity.com/paper/2501.09451/) (2025)

## Abstract (arXiv)

Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs' capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graph-based dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers.