Multi-purpose neural network for French categorial grammars

Gaëtan Margueritte, Daisuke Bekki, Koji Mineshima


Abstract
Categorial grammar (CG) is a lexicalized grammar formalism that can be used to identify and extract the semantics of natural language sentences. However, despite being used actively to solve natural language understanding tasks such as natural language inference or recognizing textual entailment, most of the tools exploiting the capacities of CG are available in a limited set of languages. This paper proposes a first step toward developing a set of tools enabling the use of CG for the French language by proposing a neural network tailored for part-of-speech and type-logical-grammar supertagging, located at the frontier between computational linguistics and artificial intelligence. Experiments show that our model can compete with state-of-the art models while retaining a simple architecture.
Anthology ID:
2023.iwcs-1.8
Volume:
Proceedings of the 15th International Conference on Computational Semantics
Month:
June
Year:
2023
Address:
Nancy, France
Editors:
Maxime Amblard, Ellen Breitholtz
Venue:
IWCS
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
78–82
Language:
URL:
https://aclanthology.org/2023.iwcs-1.8
DOI:
Bibkey:
Cite (ACL):
Gaëtan Margueritte, Daisuke Bekki, and Koji Mineshima. 2023. Multi-purpose neural network for French categorial grammars. In Proceedings of the 15th International Conference on Computational Semantics, pages 78–82, Nancy, France. Association for Computational Linguistics.
Cite (Informal):
Multi-purpose neural network for French categorial grammars (Margueritte et al., IWCS 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.iwcs-1.8.pdf