@inproceedings{moosavi-monazzah-eetemadi-2023-prodicus,
title = "Prodicus at {S}em{E}val-2023 Task 4: Enhancing Human Value Detection with Data Augmentation and Fine-Tuned Language Models",
author = "Moosavi Monazzah, Erfan and
Eetemadi, Sauleh",
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-1.279",
doi = "10.18653/v1/2023.semeval-1.279",
pages = "2033--2038",
abstract = "This paper introduces a data augmentation technique for the task of detecting human values. Our approach involves generating additional examples using metadata that describes the labels in the datasets. We evaluated the effectiveness of our method by fine-tuning BERT and RoBERTa models on our augmented dataset and comparing their F1 -scores to those of the non-augmented dataset. We obtained competitive results on both the Main test set and the Nahj al-Balagha test set, ranking 14th and 7th respectively among the participants. We also demonstrate that by incorporating our augmentation technique, the classification performance of BERT and RoBERTa is improved, resulting in an increase of up to 10.1{\%} in their F1-score.",
}
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<abstract>This paper introduces a data augmentation technique for the task of detecting human values. Our approach involves generating additional examples using metadata that describes the labels in the datasets. We evaluated the effectiveness of our method by fine-tuning BERT and RoBERTa models on our augmented dataset and comparing their F1 -scores to those of the non-augmented dataset. We obtained competitive results on both the Main test set and the Nahj al-Balagha test set, ranking 14th and 7th respectively among the participants. We also demonstrate that by incorporating our augmentation technique, the classification performance of BERT and RoBERTa is improved, resulting in an increase of up to 10.1% in their F1-score.</abstract>
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%0 Conference Proceedings
%T Prodicus at SemEval-2023 Task 4: Enhancing Human Value Detection with Data Augmentation and Fine-Tuned Language Models
%A Moosavi Monazzah, Erfan
%A Eetemadi, Sauleh
%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 moosavi-monazzah-eetemadi-2023-prodicus
%X This paper introduces a data augmentation technique for the task of detecting human values. Our approach involves generating additional examples using metadata that describes the labels in the datasets. We evaluated the effectiveness of our method by fine-tuning BERT and RoBERTa models on our augmented dataset and comparing their F1 -scores to those of the non-augmented dataset. We obtained competitive results on both the Main test set and the Nahj al-Balagha test set, ranking 14th and 7th respectively among the participants. We also demonstrate that by incorporating our augmentation technique, the classification performance of BERT and RoBERTa is improved, resulting in an increase of up to 10.1% in their F1-score.
%R 10.18653/v1/2023.semeval-1.279
%U https://aclanthology.org/2023.semeval-1.279
%U https://doi.org/10.18653/v1/2023.semeval-1.279
%P 2033-2038
Markdown (Informal)
[Prodicus at SemEval-2023 Task 4: Enhancing Human Value Detection with Data Augmentation and Fine-Tuned Language Models](https://aclanthology.org/2023.semeval-1.279) (Moosavi Monazzah & Eetemadi, SemEval 2023)
ACL