# Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training

arXiv: https://arxiv.org/abs/2010.05003 · HTML: https://mlsanity.com/paper/2010.05003/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Dependency Parsing as MRC-based Span-Span Prediction](https://mlsanity.com/paper/2105.07654/) (2021)
- [Headed-Span-Based Projective Dependency Parsing](https://mlsanity.com/paper/2108.04750/) (2021)
- [Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks](https://mlsanity.com/paper/2310.17238/) (2023)
- [Dependency Parsing with Bottom-up Hierarchical Pointer Networks](https://mlsanity.com/paper/2105.09611/) (2021)
- [Combining (second-order) graph-based and headed-span-based projective dependency parsing](https://mlsanity.com/paper/2108.05838/) (2021)
- [Sparse Fuzzy Attention for Structured Sentiment Analysis](https://mlsanity.com/paper/2109.06719/) (2021)
- [Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation](https://mlsanity.com/paper/2311.15211/) (2023)
- [Hexatagging: Projective Dependency Parsing as Tagging](https://mlsanity.com/paper/2306.05477/) (2023)
- [Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings](https://mlsanity.com/paper/2107.02416/) (2021)
- [Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective](https://mlsanity.com/paper/2305.15057/) (2023)
- [Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field Inference](https://mlsanity.com/paper/2204.03619/) (2022)
- [Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field](https://mlsanity.com/paper/2212.08929/) (2022)
- [Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing](https://mlsanity.com/paper/2407.04801/) (2024)
- [Schema-Free Dependency Parsing via Sequence Generation](https://mlsanity.com/paper/2201.12407/) (2022)
- [Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement](https://mlsanity.com/paper/2501.09451/) (2025)
- [Dependency Parsing with the Structuralized Prompt Template](https://mlsanity.com/paper/2502.16919/) (2025)

## Abstract (arXiv)

In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the accuracy of very recent state-of-the-art second-order graph-based neural dependency parsers and have significantly faster speed in both training and testing. We also empirically show the advantage of second-order parsing over first-order parsing and observe that the usefulness of the head-selection structured constraint vanishes when using BERT embedding.