# Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data

arXiv: https://arxiv.org/abs/2006.01414 · HTML: https://mlsanity.com/paper/2006.01414/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [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)
- [Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings](https://mlsanity.com/paper/2107.02416/) (2021)
- [The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task](https://mlsanity.com/paper/2107.01982/) (2021)
- [A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing](https://mlsanity.com/paper/2306.12018/) (2023)

## Abstract (arXiv)

This paper presents the system used in our submission to the textitIWPT 2020 Shared Task. Our system is a graph-based parser with second-order inference. For the low-resource Tamil corpus, we specially mixed the training data of Tamil with other languages and significantly improved the performance of Tamil. Due to our misunderstanding of the submission requirements, we submitted graphs that are not connected, which makes our system only rank 6th over 10 teams. However, after we fixed this problem, our system is 0.6 ELAS higher than the team that ranked 1st in the official results.