# Empirical Analysis for Unsupervised Universal Dependency Parse Tree Aggregation

arXiv: https://arxiv.org/abs/2403.19183 · HTML: https://mlsanity.com/paper/2403.19183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [UDapter: Language Adaptation for Truly Universal Dependency Parsing](https://mlsanity.com/paper/2004.14327/) (2020)
- [Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation](https://mlsanity.com/paper/1807.03121/) (2018)
- [Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser](https://mlsanity.com/paper/1609.07561/) (2016)
- [Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation](https://mlsanity.com/paper/2103.02761/) (2021)
- [Truth Discovery in Sequence Labels from Crowds](https://mlsanity.com/paper/2109.04470/) (2021)

## Abstract (arXiv)

Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks. Parsers' quality often varies depending on the domain and the language involved. Therefore, it is essential to combat the issue of varying quality to achieve stable performance. In various NLP tasks, aggregation methods are used for post-processing aggregation and have been shown to combat the issue of varying quality. However, aggregation methods for post-processing aggregation have not been sufficiently studied in dependency parsing tasks. In an extensive empirical study, we compare different unsupervised post-processing aggregation methods to identify the most suitable dependency tree structure aggregation method.