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
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 4: valueeval: identification of human values behind arguments Paper
TLDR:
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
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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.