@inproceedings{li-etal-2024-cot,
title = "{C}o{T}-based Data Augmentation Strategy for Persuasion Techniques Detection",
author = "Li, Dailin and
Wang, Chuhan and
Zou, Xin and
Wang, Junlong and
Chen, Peng and
Wang, Jian and
Yang, Liang and
Lin, Hongfei",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.190",
doi = "10.18653/v1/2024.semeval-1.190",
pages = "1315--1321",
abstract = "Detecting persuasive communication is an important topic in Natural Language Processing (NLP), as it can be useful in identifying fake information on social media. We have developed a system to identify applied persuasion techniques in text fragments across four languages: English, Bulgarian, North Macedonian, and Arabic. Our system uses data augmentation methods and employs an ensemble strategy that combines the strengths of both RoBERTa and DeBERTa models. Due to limited resources, we concentrated solely on task 1, and our solution achieved the top ranking in the English track during the official assessments. We also analyse the impact of architectural decisions, data constructionand training strategies.",
}
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<abstract>Detecting persuasive communication is an important topic in Natural Language Processing (NLP), as it can be useful in identifying fake information on social media. We have developed a system to identify applied persuasion techniques in text fragments across four languages: English, Bulgarian, North Macedonian, and Arabic. Our system uses data augmentation methods and employs an ensemble strategy that combines the strengths of both RoBERTa and DeBERTa models. Due to limited resources, we concentrated solely on task 1, and our solution achieved the top ranking in the English track during the official assessments. We also analyse the impact of architectural decisions, data constructionand training strategies.</abstract>
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%0 Conference Proceedings
%T CoT-based Data Augmentation Strategy for Persuasion Techniques Detection
%A Li, Dailin
%A Wang, Chuhan
%A Zou, Xin
%A Wang, Junlong
%A Chen, Peng
%A Wang, Jian
%A Yang, Liang
%A Lin, Hongfei
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F li-etal-2024-cot
%X Detecting persuasive communication is an important topic in Natural Language Processing (NLP), as it can be useful in identifying fake information on social media. We have developed a system to identify applied persuasion techniques in text fragments across four languages: English, Bulgarian, North Macedonian, and Arabic. Our system uses data augmentation methods and employs an ensemble strategy that combines the strengths of both RoBERTa and DeBERTa models. Due to limited resources, we concentrated solely on task 1, and our solution achieved the top ranking in the English track during the official assessments. We also analyse the impact of architectural decisions, data constructionand training strategies.
%R 10.18653/v1/2024.semeval-1.190
%U https://aclanthology.org/2024.semeval-1.190
%U https://doi.org/10.18653/v1/2024.semeval-1.190
%P 1315-1321
Markdown (Informal)
[CoT-based Data Augmentation Strategy for Persuasion Techniques Detection](https://aclanthology.org/2024.semeval-1.190) (Li et al., SemEval 2024)
ACL
- Dailin Li, Chuhan Wang, Xin Zou, Junlong Wang, Peng Chen, Jian Wang, Liang Yang, and Hongfei Lin. 2024. CoT-based Data Augmentation Strategy for Persuasion Techniques Detection. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1315–1321, Mexico City, Mexico. Association for Computational Linguistics.