@inproceedings{sadeghi-etal-2023-sinaai,
    title = "{S}ina{AI} at {S}em{E}val-2023 Task 3: A Multilingual Transformer Language Model-based Approach for the Detection of News Genre, Framing and Persuasion Techniques",
    author = "Sadeghi, Aryan  and
      Alipour, Reza  and
      Taeb, Kamyar  and
      Morassafar, Parimehr  and
      Salemahim, Nima  and
      Asgari, Ehsaneddin",
    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-1.300",
    doi = "10.18653/v1/2023.semeval-1.300",
    pages = "2168--2173",
    abstract = "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 italy) in sub-task 1 and one language (Russian) for sub-task 2.",
}

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 italy) in sub-task 1 and one language (Russian) for sub-task 2.
Sadeghi, Aryan Alipour, Reza Taeb, Kamyar Morassafar, Parimehr Salemahim, Nima Asgari, Ehsaneddin
SinaAI at SemEval-2023 Task 3: A Multilingual Transformer Language Model-based Approach for the Detection of News Genre, Framing and Persuasion Techniques