NAACL 2025short0 citations
Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement
Nicolas Floquet, Joseph Le Roux, Nadi Tomeh, Thierry Charnois
Abstract
We propose a novel architecture for graph-based dependency parsing that explicitly constructs vectors, from which both arcs and labels are scored. Our method addresses key limitations of the standard two-pipeline approach by unifying arc scoring and labeling into a single network, reducing scalability issues caused by the information bottleneck and lack of parameter sharing. Additionally, our architecture overcomes limited arc interactions with transformer layers to efficiently simulate higher-order dependencies. Experiments on PTB and UD show that our model outperforms state-of-the-art parsers in both accuracy and efficiency.
BibTeX
@inproceedings{floquet-etal-2025-scaling,
title = "Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement",
author = "Floquet, Nicolas and
Roux, Joseph Le and
Tomeh, Nadi and
Charnois, Thierry",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-short.60/",
pages = "722--734",
ISBN = "979-8-89176-190-2"
}