@inproceedings{liao-etal-2023-marseclipse,
    title = "{M}ars{E}clipse at {S}em{E}val-2023 Task 3: Multi-lingual and Multi-label Framing Detection with Contrastive Learning",
    author = "Liao, Qisheng  and
      Lai, Meiting  and
      Nakov, Preslav",
    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://preview.aclanthology.org/fix-sig-urls/2023.semeval-1.10/",
    doi = "10.18653/v1/2023.semeval-1.10",
    pages = "83--87",
    abstract = "This paper describes our system for SemEval-2023 Task 3 Subtask 2 on Framing Detection. We used a multi-label contrastive loss for fine-tuning large pre-trained language models in a multi-lingual setting, achieving very competitive results: our system was ranked first on the official test set and on the official shared task leaderboard for five of the six languages for which we had training data and for which we could perform fine-tuning. Here, we describe our experimental setup, as well as various ablation studies. The code of our system is available at \url{https://github.com/QishengL/SemEval2023}."
}