@inproceedings{schrader-etal-2023-boschai,
    title = "{B}osch{AI} @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation",
    author = "Schrader, Timo Pierre  and
      Razniewski, Simon  and
      Lange, Lukas  and
      Friedrich, Annemarie",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Tanev, Hristo  and
      Zavarella, Vanni  and
      Yeniterzi, Reyyan  and
      Y{\"o}r{\"u}k, Erdem  and
      Slavcheva, Milena},
    booktitle = "Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.case-1.5/",
    pages = "38--43",
    abstract = "Understanding causality is a core aspect of intelligence. The Event Causality Identification with Causal News Corpus Shared Task addresses two aspects of this challenge: Subtask 1 aims at detecting causal relationships in texts, and Subtask 2 requires identifying signal words and the spans that refer to the cause or effect, respectively. Our system, which is based on pre-trained transformers, stacked sequence tagging, and synthetic data augmentation, ranks third in Subtask 1 and wins Subtask 2 with an F1 score of 72.8, corresponding to a margin of 13 pp. to the second-best system."
}