CCG Revisited: A Multilingual Empirical Study of the Kuhlmann-Satta Algorithm

Paul He, Gerald Penn


Abstract
We revisit the polynomial-time CCG parsing algorithm introduced by Kuhlmann & Satta (2014), and provide a publicly available implementation of it. We evaluate its empirical performance against a naive CKY-style parser across the Parallel Meaning Bank (PMB) corpus. While the fast parser is slightly slower on average, relative to the size of the PMB, but the trend improves as a function of sentence length, and the PMB is large enough to witness an inversion. Our analysis quantifies this crossover and highlights the importance of derivational context decomposition in practical parsing scenarios.
Anthology ID:
2025.iwpt-1.3
Volume:
Proceedings of the 18th International Conference on Parsing Technologies (IWPT, SyntaxFest 2025)
Month:
August
Year:
2025
Address:
Ljubljana, Slovenia
Editors:
Kenji Sagae, Stephan Oepen
Venues:
IWPT | SyntaxFest
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
20–25
Language:
URL:
https://preview.aclanthology.org/mtsummit-25-ingestion/2025.iwpt-1.3/
DOI:
Bibkey:
Cite (ACL):
Paul He and Gerald Penn. 2025. CCG Revisited: A Multilingual Empirical Study of the Kuhlmann-Satta Algorithm. In Proceedings of the 18th International Conference on Parsing Technologies (IWPT, SyntaxFest 2025), pages 20–25, Ljubljana, Slovenia. Association for Computational Linguistics.
Cite (Informal):
CCG Revisited: A Multilingual Empirical Study of the Kuhlmann-Satta Algorithm (He & Penn, IWPT-SyntaxFest 2025)
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PDF:
https://preview.aclanthology.org/mtsummit-25-ingestion/2025.iwpt-1.3.pdf