@inproceedings{costa-etal-2023-clac,
title = "{CL}a{C} at {S}em{E}val-2023 Task 3: Language Potluck {R}o{BERT}a Detects Online Persuasion Techniques in a Multilingual Setup",
author = "Costa, Nelson Filipe and
Hamilton, Bryce and
Kosseim, Leila",
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-1.223",
doi = "10.18653/v1/2023.semeval-1.223",
pages = "1613--1618",
abstract = "This paper presents our approach to the SemEval-2023 Task 3 to detect online persuasion techniques in a multilingual setup. Our classification system is based on the RoBERTa-base model trained predominantly on English to label the persuasion techniques across 9 different languages. Our system was able to significantly surpass the baseline performance in 3 of the 9 languages: English, Georgian and Greek. However, our wrong assumption that a single classification system trained predominantly on English could generalize well to other languages, negatively impacted our scores on the other 6 languages. In this paper, we provide a description of the reasoning behind the development of our final model and what conclusions may be drawn from its performance for future work.",
}
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<abstract>This paper presents our approach to the SemEval-2023 Task 3 to detect online persuasion techniques in a multilingual setup. Our classification system is based on the RoBERTa-base model trained predominantly on English to label the persuasion techniques across 9 different languages. Our system was able to significantly surpass the baseline performance in 3 of the 9 languages: English, Georgian and Greek. However, our wrong assumption that a single classification system trained predominantly on English could generalize well to other languages, negatively impacted our scores on the other 6 languages. In this paper, we provide a description of the reasoning behind the development of our final model and what conclusions may be drawn from its performance for future work.</abstract>
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%0 Conference Proceedings
%T CLaC at SemEval-2023 Task 3: Language Potluck RoBERTa Detects Online Persuasion Techniques in a Multilingual Setup
%A Costa, Nelson Filipe
%A Hamilton, Bryce
%A Kosseim, Leila
%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 costa-etal-2023-clac
%X This paper presents our approach to the SemEval-2023 Task 3 to detect online persuasion techniques in a multilingual setup. Our classification system is based on the RoBERTa-base model trained predominantly on English to label the persuasion techniques across 9 different languages. Our system was able to significantly surpass the baseline performance in 3 of the 9 languages: English, Georgian and Greek. However, our wrong assumption that a single classification system trained predominantly on English could generalize well to other languages, negatively impacted our scores on the other 6 languages. In this paper, we provide a description of the reasoning behind the development of our final model and what conclusions may be drawn from its performance for future work.
%R 10.18653/v1/2023.semeval-1.223
%U https://aclanthology.org/2023.semeval-1.223
%U https://doi.org/10.18653/v1/2023.semeval-1.223
%P 1613-1618
Markdown (Informal)
[CLaC at SemEval-2023 Task 3: Language Potluck RoBERTa Detects Online Persuasion Techniques in a Multilingual Setup](https://aclanthology.org/2023.semeval-1.223) (Costa et al., SemEval 2023)
ACL