@inproceedings{song-etal-2023-arthur,
title = "Arthur Caplan at {S}em{E}val-2023 Task 4: Enhancing Human Value Detection through Fine-tuned Pre-trained Models",
author = "Song, Xianxian and
Zhao, Jinhui and
Cao, Ruiqi and
Sui, Linchi and
Li, Binyang and
Guan, Tingyue",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.268",
doi = "10.18653/v1/2023.semeval-1.268",
pages = "1953--1959",
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.",
}
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%0 Conference Proceedings
%T Arthur Caplan at SemEval-2023 Task 4: Enhancing Human Value Detection through Fine-tuned Pre-trained Models
%A Song, Xianxian
%A Zhao, Jinhui
%A Cao, Ruiqi
%A Sui, Linchi
%A Li, Binyang
%A Guan, Tingyue
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F song-etal-2023-arthur
%X 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.
%R 10.18653/v1/2023.semeval-1.268
%U https://aclanthology.org/2023.semeval-1.268
%U https://doi.org/10.18653/v1/2023.semeval-1.268
%P 1953-1959
Markdown (Informal)
[Arthur Caplan at SemEval-2023 Task 4: Enhancing Human Value Detection through Fine-tuned Pre-trained Models](https://aclanthology.org/2023.semeval-1.268) (Song et al., SemEval 2023)
ACL