# Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency Parsing

arXiv: https://arxiv.org/abs/2412.11543 · HTML: https://mlsanity.com/paper/2412.11543/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Ensembling Membership Inference Attacks Against Tabular Generative Models](https://mlsanity.com/paper/2509.05350/) (2025)

## Abstract (arXiv)

We address unsupervised dependency parsing by building an ensemble of diverse existing models through post hoc aggregation of their output dependency parse structures. We observe that these ensembles often suffer from low robustness against weak ensemble components due to error accumulation. To tackle this problem, we propose an efficient ensemble-selection approach that considers error diversity and avoids error accumulation. Results demonstrate that our approach outperforms each individual model as well as previous ensemble techniques. Additionally, our experiments show that the proposed ensemble-selection method significantly enhances the performance and robustness of our ensemble, surpassing previously proposed strategies, which have not accounted for error diversity.