Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach

Deniz Ekin Yavas, Laura Kallmeyer, Rainer Osswald, Elisabetta Jezek, Marta Ricchiardi, Long Chen


Abstract
Identifying semantic argument types in predication contexts is not a straightforward task for several reasons, such as inherent polysemy, coercion, and copredication phenomena. In this paper, we train monolingual and multilingual classifiers with a zero-shot cross-lingual approach to identify semantic argument types in predications using pre-trained language models as feature extractors. We train classifiers for different semantic argument types and for both verbal and adjectival predications. Furthermore, we propose a method to detect copredication using these classifiers through identifying the argument semantic type targeted in different predications over the same noun in a sentence. We evaluate the performance of the method on copredication test data with Food•Event nouns for 5 languages.
Anthology ID:
2023.ranlp-1.35
Volume:
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
Month:
September
Year:
2023
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:
310–320
Language:
URL:
https://aclanthology.org/2023.ranlp-1.35
DOI:
Bibkey:
Cite (ACL):
Deniz Ekin Yavas, Laura Kallmeyer, Rainer Osswald, Elisabetta Jezek, Marta Ricchiardi, and Long Chen. 2023. Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 310–320, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
Cite (Informal):
Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach (Yavas et al., RANLP 2023)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.ranlp-1.35.pdf