# Graph-to-Graph Transformer for Transition-based Dependency Parsing

arXiv: https://arxiv.org/abs/1911.03561 · HTML: https://mlsanity.com/paper/1911.03561/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 13 of 13)

- [GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation](https://mlsanity.com/paper/2203.00949/) (2022)
- [Pushing the Limits of ChatGPT on NLP Tasks](https://mlsanity.com/paper/2306.09719/) (2023)
- [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://mlsanity.com/paper/2211.01482/) (2022)
- [Dependency Parsing as MRC-based Span-Span Prediction](https://mlsanity.com/paper/2105.07654/) (2021)
- [Syntax-Aware Graph-to-Graph Transformer for Semantic Role Labelling](https://mlsanity.com/paper/2104.07704/) (2021)
- [Graph Refinement for Coreference Resolution](https://mlsanity.com/paper/2203.16574/) (2022)
- [Transformers as Graph-to-Graph Models](https://mlsanity.com/paper/2310.17936/) (2023)
- [Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers](https://mlsanity.com/paper/2205.11456/) (2022)
- [Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks](https://mlsanity.com/paper/2506.14098/) (2025)
- [GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction](https://mlsanity.com/paper/2308.14423/) (2023)
- [Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction](https://mlsanity.com/paper/2107.06905/) (2021)
- [Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors](https://mlsanity.com/paper/2408.06778/) (2024)
- [CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages](https://mlsanity.com/paper/2410.06944/) (2024)

## Abstract (arXiv)

We propose the Graph2Graph Transformer architecture for conditioning on and predicting arbitrary graphs, and apply it to the challenging task of transition-based dependency parsing. After proposing two novel Transformer models of transition-based dependency parsing as strong baselines, we show that adding the proposed mechanisms for conditioning on and predicting graphs of Graph2Graph Transformer results in significant improvements, both with and without BERT pre-training. The novel baselines and their integration with Graph2Graph Transformer significantly outperform the state-of-the-art in traditional transition-based dependency parsing on both English Penn Treebank, and 13 languages of Universal Dependencies Treebanks. Graph2Graph Transformer can be integrated with many previous structured prediction methods, making it easy to apply to a wide range of NLP tasks.