@inproceedings{ivacic-etal-2024-comparing,
    title = "Comparing News Framing of Migration Crises using Zero-Shot Classification",
    author = "Iva{\v{c}}i{\v{c}}, Nikola  and
      Purver, Matthew  and
      Lind, Fabienne  and
      Pollak, Senja  and
      Boomgaarden, Hajo  and
      Bajt, Veronika",
    editor = "Sommerauer, Pia  and
      Caselli, Tommaso  and
      Nissim, Malvina  and
      Remijnse, Levi  and
      Vossen, Piek",
    booktitle = "Proceedings of the First Workshop on Reference, Framing, and Perspective @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.rfp-1.3/",
    pages = "18--27",
    abstract = "We present an experiment on classifying news frames in a language unseen by the learner, using zero-shot cross-lingual transfer learning. We used two pre-trained multilingual Transformer Encoder neural network models and tested with four specific news frames, investigating two approaches to the resulting multi-label task: Binary Relevance (treating each frame independently) and Label Power-set (predicting each possible combination of frames). We train our classifiers on an available annotated multilingual migration news dataset and test on an unseen Slovene language migration news corpus, first evaluating performance and then using the classifiers to analyse how media framed the news during the periods of Syria and Ukraine conflict-related migrations."
}