Arthur Caplan at SemEval-2023 Task 4: Enhancing Human Value Detection through Fine-tuned Pre-trained Models

Xianxian Song, Jinhui Zhao, Ruiqi Cao, Linchi Sui, Binyang Li, Tingyue Guan


Abstract
The computational identification of human values is a novel and challenging research that holds the potential to offer valuable insights into the nature of human behavior and cognition. This paper presents the methodology adopted by the Arthur-Caplan research team for the SemEval-2023 Task 4, which entailed the detection of human values behind arguments. The proposed system integrates BERT, ERNIE2.0, RoBERTA and XLNet models with fine tuning. Experimental results show that the macro F1 score of our system achieved 0.512, which overperformed baseline methods by 9.2% on the test set.
Anthology ID:
2023.semeval-1.268
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:
1953–1959
Language:
URL:
https://aclanthology.org/2023.semeval-1.268
DOI:
10.18653/v1/2023.semeval-1.268
Bibkey:
Cite (ACL):
Xianxian Song, Jinhui Zhao, Ruiqi Cao, Linchi Sui, Binyang Li, and Tingyue Guan. 2023. Arthur Caplan at SemEval-2023 Task 4: Enhancing Human Value Detection through Fine-tuned Pre-trained Models. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1953–1959, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Arthur Caplan at SemEval-2023 Task 4: Enhancing Human Value Detection through Fine-tuned Pre-trained Models (Song et al., SemEval 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.semeval-1.268.pdf