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SinaAI at semeval-2023 task 3: a multilingual transformer language model-based approach for the detection of news genre, framing and persuasion techniques

Sadeghi, A ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.18653/v1/2023.semeval-1.300
  3. Publisher: Association for Computational Linguistics , 2023
  4. Abstract:
  5. 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. © 2023 Association for Computational Linguistics
  6. Keywords:
  7. Augmentation techniques ; Data augmentation ; Fine tuning ; Genre detection ; Language model ; Model based approach ; Multiple languages ; News articles ; Performance ; Subtask
  8. Source: 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop ; 2023 , Pages 2168-2173 ; 978-195942999-9 (ISBN)
  9. URL: https://aclanthology.org/2023.semeval-1.300