CSECU-DSG at SemEval-2023 Task 4: Fine-tuning DeBERTa Transformer Model with Cross-fold Training and Multi-sample Dropout for Human Values Identification

Abdul Aziz, Md. Akram Hossain, Abu Nowshed Chy

Abstract
Human values identification from a set of argument is becoming a prominent area of research in argument mining. Among some options, values convey what may be the most desirable and widely accepted answer. The diversity of human beliefs, random texture and implicit meaning within the arguments makes it more difficult to identify human values from the arguments. To address these challenges, SemEval-2023 Task 4 introduced a shared task ValueEval focusing on identifying human values categories based on given arguments. This paper presents our participation in this task where we propose a finetuned DeBERTa transformers-based classification approach to identify the desire human value category. We utilize different training strategy with the finetuned DeBERTa model to enhance contextual representation on this downstream task. Our proposed method achieved competitive performance among the participants’ methods.
Anthology ID:
2023.semeval-1.274
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:
1988–1994
Language:
URL:
https://aclanthology.org/2023.semeval-1.274
DOI:
10.18653/v1/2023.semeval-1.274
Bibkey:
Cite (ACL):
Abdul Aziz, Md. Akram Hossain, and Abu Nowshed Chy. 2023. CSECU-DSG at SemEval-2023 Task 4: Fine-tuning DeBERTa Transformer Model with Cross-fold Training and Multi-sample Dropout for Human Values Identification. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1988–1994, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
CSECU-DSG at SemEval-2023 Task 4: Fine-tuning DeBERTa Transformer Model with Cross-fold Training and Multi-sample Dropout for Human Values Identification (Aziz et al., SemEval 2023)
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