Hierarchical Bracketing Encodings for Dependency Parsing as Tagging

Ana Ezquerro, David Vilares, Anssi Yli-Jyrä, Carlos Gómez-Rodríguez


Abstract
We present a family of encodings for sequence labeling dependency parsing, based on the concept of hierarchical bracketing. We show that the existing 4-bit projective encoding belongs to this family, but it is suboptimal in the number of labels used to encode a tree. We derive an optimal hierarchical bracketing, which minimizes the number of symbols used and encodes projective trees using only 12 distinct labels (vs. 16 for the 4-bit encoding). We also extend optimal hierarchical bracketing to support arbitrary non-projectivity in a more compact way than previous encodings. Our new encodings yield competitive accuracy on a diverse set of treebanks.
Anthology ID:
2025.acl-long.903
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
18436–18450
Language:
URL:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.903/
DOI:
Bibkey:
Cite (ACL):
Ana Ezquerro, David Vilares, Anssi Yli-Jyrä, and Carlos Gómez-Rodríguez. 2025. Hierarchical Bracketing Encodings for Dependency Parsing as Tagging. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18436–18450, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
Hierarchical Bracketing Encodings for Dependency Parsing as Tagging (Ezquerro et al., ACL 2025)
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PDF:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.903.pdf