Abstract
This paper presents a hierarchical similarity-aware approach for the SemEval-2023 task 4 human value detection behind arguments using SBERT. The approach takes similarity score as an additional source of information between the input arguments and the lower level of labels in a human value hierarchical dataset. Our similarity-aware model improved the similarity-agnostic baseline model, especially showing a significant increase in or the value categories with lowest scores by the baseline model.- Anthology ID:
- 2023.semeval-1.188
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1359–1364
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.188
- DOI:
- 10.18653/v1/2023.semeval-1.188
- Cite (ACL):
- Sumire Honda and Sebastian Wilharm. 2023. Noam Chomsky at SemEval-2023 Task 4: Hierarchical Similarity-aware Model for Human Value Detection. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1359–1364, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Noam Chomsky at SemEval-2023 Task 4: Hierarchical Similarity-aware Model for Human Value Detection (Honda & Wilharm, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.semeval-1.188.pdf