StFX-NLP at SemEval-2023 Task 4: Unsupervised and Supervised Approaches to Detecting Human Values in Arguments

Ethan Heavey, Milton King, James Hughes


Abstract
In this paper, we discuss our models applied to Task 4: Human Value Detection of SemEval 2023, which incorporated two different embedding techniques to interpret the data. Preliminary experiments were conducted to observe important word types. Subsequently, we explored an XGBoost model, an unsupervised learning model, and two Ensemble learning models were then explored. The best performing model, an ensemble model employing a soft voting technique, secured the 34th spot out of 39 teams, on a class imbalanced dataset. We explored the inclusion of different parts of the provided knowledge resource and found that considering only specific parts assisted our models.
Anthology ID:
2023.semeval-1.29
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:
205–211
Language:
URL:
https://aclanthology.org/2023.semeval-1.29
DOI:
10.18653/v1/2023.semeval-1.29
Bibkey:
Cite (ACL):
Ethan Heavey, Milton King, and James Hughes. 2023. StFX-NLP at SemEval-2023 Task 4: Unsupervised and Supervised Approaches to Detecting Human Values in Arguments. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 205–211, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
StFX-NLP at SemEval-2023 Task 4: Unsupervised and Supervised Approaches to Detecting Human Values in Arguments (Heavey et al., SemEval 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.semeval-1.29.pdf