# Efficient EUD Parsing

arXiv: https://arxiv.org/abs/2006.00838 · HTML: https://mlsanity.com/paper/2006.00838/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021](https://mlsanity.com/paper/2106.04216/) (2021)
- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
- [Replicating and Extending "Because Their Treebanks Leak": Graph Isomorphism, Covariants, and Parser Performance](https://mlsanity.com/paper/2106.00352/) (2021)
- [Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling](https://mlsanity.com/paper/2103.08955/) (2021)
- [TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning](https://mlsanity.com/paper/2107.06907/) (2021)
- [Splitting EUD graphs into trees: A quick and clatty approach](https://mlsanity.com/paper/2106.13155/) (2021)
- [The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task](https://mlsanity.com/paper/2107.01982/) (2021)
- [The Impact of Edge Displacement Vaserstein Distance on UD Parsing Performance](https://mlsanity.com/paper/2209.07139/) (2022)

## Abstract (arXiv)

We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2020. We engaged with the task by focusing on efficiency. For this we considered training costs and inference efficiency. Our models are a combination of distilled neural dependency parsers and a rule-based system that projects UD trees into EUD graphs. We obtained an average ELAS of 74.04 for our official submission, ranking 4th overall.