TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Dependency Parsing	Universal Dependencies	Stanford+	LAS	74.16	# 4
Dependency Parsing	Universal Dependencies	Stanford+	UAS	62.08	# 3
Dependency Parsing	Universal Dependencies	Stanford+	BLEX	65.28	# 2

Badge	Markdown
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/universal-dependency-parsing-from-scratch/dependency-parsing-on-universal-dependencies)](https://paperswithcode.com/sota/dependency-parsing-on-universal-dependencies?p=universal-dependency-parsing-from-scratch)`

Universal Dependency Parsing from Scratch

CONLL 2018 · Peng Qi, Timothy Dozat, Yuhao Zhang, Christopher D. Manning ·

This paper describes Stanford's system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation, to POS tagging and dependency parsing. Our single system submission achieved very competitive performance on big treebanks. Moreover, after fixing an unfortunate bug, our corrected system would have placed the 2nd, 1st, and 3rd on the official evaluation metrics LAS,MLAS, and BLEX, and would have outperformed all submission systems on low-resource treebank categories on all metrics by a large margin. We further show the effectiveness of different model components through extensive ablation studies.