@inproceedings{qachfar-verma-2023-redaspersuasion,
title = "{R}e{DASP}ersuasion at {S}em{E}val-2023 Task 3: Persuasion Detection using Multilingual Transformers and Language Agnostic Features",
author = "Qachfar, Fatima Zahra and
Verma, Rakesh",
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-1.293",
doi = "10.18653/v1/2023.semeval-1.293",
pages = "2124--2132",
abstract = "This paper describes a multilingual persuasion detection system that incorporates persuasion technique attributes for a multi-label classification task. The proposed method has two advantages. First, it combines persuasion features with a sequence classification transformer to classify persuasion techniques. Second, it is a language agnostic approach that supports a total of 100 languages, guaranteed by the multilingual transformer module and the Google translator interface. We found that our persuasion system outperformed the SemEval baseline in all languages except zero shot prediction languages, which did not constitute the main focus of our research. With the highest F1-Micro score of 0.45, Italian achieved the eighth position on the leaderboard.",
}
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<abstract>This paper describes a multilingual persuasion detection system that incorporates persuasion technique attributes for a multi-label classification task. The proposed method has two advantages. First, it combines persuasion features with a sequence classification transformer to classify persuasion techniques. Second, it is a language agnostic approach that supports a total of 100 languages, guaranteed by the multilingual transformer module and the Google translator interface. We found that our persuasion system outperformed the SemEval baseline in all languages except zero shot prediction languages, which did not constitute the main focus of our research. With the highest F1-Micro score of 0.45, Italian achieved the eighth position on the leaderboard.</abstract>
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%0 Conference Proceedings
%T ReDASPersuasion at SemEval-2023 Task 3: Persuasion Detection using Multilingual Transformers and Language Agnostic Features
%A Qachfar, Fatima Zahra
%A Verma, Rakesh
%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 qachfar-verma-2023-redaspersuasion
%X This paper describes a multilingual persuasion detection system that incorporates persuasion technique attributes for a multi-label classification task. The proposed method has two advantages. First, it combines persuasion features with a sequence classification transformer to classify persuasion techniques. Second, it is a language agnostic approach that supports a total of 100 languages, guaranteed by the multilingual transformer module and the Google translator interface. We found that our persuasion system outperformed the SemEval baseline in all languages except zero shot prediction languages, which did not constitute the main focus of our research. With the highest F1-Micro score of 0.45, Italian achieved the eighth position on the leaderboard.
%R 10.18653/v1/2023.semeval-1.293
%U https://aclanthology.org/2023.semeval-1.293
%U https://doi.org/10.18653/v1/2023.semeval-1.293
%P 2124-2132
Markdown (Informal)
[ReDASPersuasion at SemEval-2023 Task 3: Persuasion Detection using Multilingual Transformers and Language Agnostic Features](https://aclanthology.org/2023.semeval-1.293) (Qachfar & Verma, SemEval 2023)
ACL