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        <title>Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement</title>
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            <namePart type="given">Luis</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement
%A Floquet, Nicolas
%A Roux, Joseph Le
%A Tomeh, Nadi
%A Charnois, Thierry
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-190-2
%F floquet-etal-2025-scaling
%X 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.
%R 10.18653/v1/2025.naacl-short.60
%U https://aclanthology.org/2025.naacl-short.60/
%U https://doi.org/10.18653/v1/2025.naacl-short.60
%P 722-734

Markdown (Informal)

[Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement](https://aclanthology.org/2025.naacl-short.60/) (Floquet et al., NAACL 2025)

• Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement (Floquet et al., NAACL 2025)

ACL

• Nicolas Floquet, Joseph Le Roux, Nadi Tomeh, and Thierry Charnois. 2025. Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement. In 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), pages 722–734, Albuquerque, New Mexico. Association for Computational Linguistics.