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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.ranlp-1.35.pdf