@inproceedings{kent-krumbiegel-2021-case,
    title = "{CASE} 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned {R}o{BERT}a Document Embeddings",
    author = "Kent, Samantha  and
      Krumbiegel, Theresa",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali},
    booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.case-1.26/",
    doi = "10.18653/v1/2021.case-1.26",
    pages = "208--217",
    abstract = "We present our submission to Task 2 of the Socio-political and Crisis Events Detection Shared Task at the CASE @ ACL-IJCNLP 2021 workshop. The task at hand aims at the fine-grained classification of socio-political events. Our best model was a fine-tuned RoBERTa transformer model using document embeddings. The corpus consisted of a balanced selection of sub-events extracted from the ACLED event dataset. We achieved a macro F-score of 0.923 and a micro F-score of 0.932 during our preliminary experiments on a held-out test set. The same model also performed best on the shared task test data (weighted F-score = 0.83). To analyze the results we calculated the topic compactness of the commonly misclassified events and conducted an error analysis."
}