# Kopsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding

arXiv: https://arxiv.org/abs/2005.12094 · HTML: https://mlsanity.com/paper/2005.12094/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 4 of 4)

- [Does injecting linguistic structure into language models lead to better alignment with brain recordings?](https://mlsanity.com/paper/2101.12608/) (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)
- [The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task](https://mlsanity.com/paper/2107.01982/) (2021)

## Abstract (arXiv)

We present Kopsala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020. Our system is a pipeline consisting of off-the-shelf models for everything but enhanced graph parsing, and for the latter, a transition-based graph parser adapted from Che et al. (2019). We train a single enhanced parser model per language, using gold sentence splitting and tokenization for training, and rely only on tokenized surface forms and multilingual BERT for encoding. While a bug introduced just before submission resulted in a severe drop in precision, its post-submission fix would bring us to 4th place in the official ranking, according to average ELAS. Our parser demonstrates that a unified pipeline is effective for both Meaning Representation Parsing and Enhanced Universal Dependencies.