@inproceedings{devatine-etal-2023-melodi,
    title = "{MELODI} at {S}em{E}val-2023 Task 3: In-domain Pre-training for Low-resource Classification of News Articles",
    author = "Devatine, Nicolas  and
      Muller, Philippe  and
      Braud, Chlo{\'e}",
    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-1.14",
    doi = "10.18653/v1/2023.semeval-1.14",
    pages = "108--113",
    abstract = "This paper describes our approach to Subtask 1 {``}News Genre Categorization{''} of SemEval-2023 Task 3 {``}Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup{''}, which aims to determine whether a given news article is an opinion piece, an objective report, or satirical. We fine-tuned the domain-specific language model POLITICS, which was pre-trained on a large-scale dataset of more than 3.6M English political news articles following ideology-driven pre-training objectives. In order to use it in the multilingual setup of the task, we added as a pre-processing step the translation of all documents into English. Our system ranked among the top systems overall in most language, and ranked 1st on the English dataset.",
}

This paper describes our approach to Subtask 1 {``}News Genre Categorization{''} of SemEval-2023 Task 3 {``}Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup{''}, which aims to determine whether a given news article is an opinion piece, an objective report, or satirical. We fine-tuned the domain-specific language model POLITICS, which was pre-trained on a large-scale dataset of more than 3.6M English political news articles following ideology-driven pre-training objectives. In order to use it in the multilingual setup of the task, we added as a pre-processing step the translation of all documents into English. Our system ranked among the top systems overall in most language, and ranked 1st on the English dataset.
Devatine, Nicolas Muller, Philippe Braud, Chlo{\'e}
MELODI at SemEval-2023 Task 3: In-domain Pre-training for Low-resource Classification of News Articles