TY - GEN
T1 - SinaAI at SemEval-2023 Task 3
T2 - 17th International Workshop on Semantic Evaluation, SemEval 2023, co-located with the 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
AU - Sadeghi, Aryan
AU - Alipour, Reza
AU - Taeb, Kamyar
AU - Morassafar, Parimehr
AU - Salemahim, Nima
AU - Asgari, Ehsaneddin
N1 - Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - This paper describes SinaAI’s participation in SemEval-2023 Task 3, which involves detecting propaganda in news articles across multiple languages. The task comprises three sub-tasks: (i) genre detection, (ii) news framing, and (iii) persuasion technique identification. The employed dataset includes news articles in nine languages and domains, including English, French, Italian, German, Polish, Russian, Georgian, Greek, and Spanish, with labeled instances of news framing, genre, and persuasion techniques. Our approach combines fine-tuning multilingual language models such as XLM, LaBSE, and mBERT with data augmentation techniques. Our experimental results show that XLM outperforms other models in terms of F1-Micro in and F1-Macro, and the ensemble of XLM and LaBSE achieved the best performance. Our study highlights the effectiveness of multilingual sentence embedding models in multilingual propaganda detection. Our models achieved highest score for two languages (Greek and Italian) in sub-task 1 and one language (Russian) for sub-task 2.
AB - This paper describes SinaAI’s participation in SemEval-2023 Task 3, which involves detecting propaganda in news articles across multiple languages. The task comprises three sub-tasks: (i) genre detection, (ii) news framing, and (iii) persuasion technique identification. The employed dataset includes news articles in nine languages and domains, including English, French, Italian, German, Polish, Russian, Georgian, Greek, and Spanish, with labeled instances of news framing, genre, and persuasion techniques. Our approach combines fine-tuning multilingual language models such as XLM, LaBSE, and mBERT with data augmentation techniques. Our experimental results show that XLM outperforms other models in terms of F1-Micro in and F1-Macro, and the ensemble of XLM and LaBSE achieved the best performance. Our study highlights the effectiveness of multilingual sentence embedding models in multilingual propaganda detection. Our models achieved highest score for two languages (Greek and Italian) in sub-task 1 and one language (Russian) for sub-task 2.
UR - http://www.scopus.com/inward/record.url?scp=85175142013&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85175142013
T3 - 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop
SP - 2168
EP - 2173
BT - 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop
A2 - Ojha, Atul Kr.
A2 - Dogruoz, A. Seza
A2 - Da San Martino, Giovanni
A2 - Madabushi, Harish Tayyar
A2 - Kumar, Ritesh
A2 - Sartori, Elisa
PB - Association for Computational Linguistics
Y2 - 13 July 2023 through 14 July 2023
ER -