@inproceedings{su-etal-2025-dynamic,
    title = "Dynamic Energy-Based Contrastive Learning with Multi-Stage Knowledge Verification for Event Causality Identification",
    author = "Su, Ya  and
      Zhang, Hu  and
      Fan, Yue  and
      Zhang, Guangjun  and
      Wang, YuJie  and
      Li, Ru  and
      Tan, Hongye",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.616/",
    pages = "12260--12278",
    ISBN = "979-8-89176-332-6",
    abstract = "Event Causal Identification (ECI) aims to identify fine-grained causal relationships between events from unstructured text. Contrastive learning has shown promise in enhancing ECI by optimizing representation distances between positive and negative samples. However, existing methods often rely on rule-based or random sampling strategies, which may introduce spurious causal positives. Moreover, static negative samples often fail to approximate actual decision boundaries, thus limiting discriminative performance. Therefore, we propose an ECI method enhanced by Dynamic Energy-based Contrastive Learning with multi-stage knowledge Verification (DECLV). Specifically, we integrate multi-source knowledge validation and LLM-driven causal inference to construct a multi-stage knowledge validation mechanism, which generates high-quality contrastive samples and effectively suppresses spurious causal disturbances. Meanwhile, we introduce the Stochastic Gradient Langevin Dynamics (SGLD) method to dynamically generate adversarial negative samples, and employ an energy-based function to model the causal boundary between positive and negative samples. The experimental results show that our method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank."
}