@article{rebboud-etal-2026-causalsense,
    title = "{C}ausal{S}ense: Leveraging Common Sense Knowledge and {LLM}s for Joint Event Extraction and Relation Classification",
    author = "REBBOUD, Youssra  and
      Lisena, Pasquale  and
      Troncy, Raphael",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.604/",
    pages = "7619--7630",
    abstract = "Event Relation Extraction (ERE) aims to identify and classify semantic relationships between events expressed in text. While existing work has mainly addressed temporal or simple causal links, fine-grained causal relations such as enable, prevent, and intend remain insufficiently explored, partly due to limited and imbalanced labeled datasets. We present a novel framework that leverages large language models (LLMs) and common-sense knowledge to jointly perform event extraction and relation classification. Our contribution includes (1) the creation of the CausalSense large-scale dataset containing more than 500k sentences from news data and commonsense knowledge extracted from ATOMIC, and enriched synthetically; and (2) the evaluation of multiple architectures, including transformer-based models and end-to-end multitask systems for extracting fine-grained causal relationships. Experimental results show that our best-performing model achieves a 32.3{\%} improvement in average F1-score over the current state of the art. The integration of commonsense knowledge substantially enhances fine-grained causal relation detection. The CausalSense dataset, our code and models are released as open source to support future research on causal event relationship extraction."
}