@inproceedings{luo-etal-2024-open,
    title = "Open Event Causality Extraction by the Assistance of {LLM} in Task Annotation, Dataset, and Method",
    author = "Luo, Kun  and
      Zhou, Tong  and
      Chen, Yubo  and
      Zhao, Jun  and
      Liu, Kang",
    editor = "Dong, Tiansi  and
      Hinrichs, Erhard  and
      Han, Zhen  and
      Liu, Kang  and
      Song, Yangqiu  and
      Cao, Yixin  and
      Hempelmann, Christian F.  and
      Sifa, Rafet",
    booktitle = "Proceedings of the Workshop: Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.neusymbridge-1.4",
    pages = "33--44",
    abstract = "Event Causality Extraction (ECE) aims to extract explicit causal relations between event pairs from the text. However, the event boundary deviation and the causal event pair mismatching are two crucial challenges that remain unaddressed. To address the above issues, we propose a paradigm to utilize LLM to optimize the task definition, evolve the datasets, and strengthen our proposed customized Contextual Highlighting Event Causality Extraction framework (CHECE). Specifically in CHECE, we propose an Event Highlighter and an Event Concretization Module, guiding the model to represent the event by a higher-level cluster and consider its causal counterpart in event boundary prediction to deal with event boundary deviation. And we propose a Contextual Event Causality Matching mechanism, meanwhile, applying LLM to diversify the content templates to force the model to learn causality from context to targeting on causal event pair mismatching. Experimental results on two ECE datasets demonstrate the effectiveness of our method.",
}

Event Causality Extraction (ECE) aims to extract explicit causal relations between event pairs from the text. However, the event boundary deviation and the causal event pair mismatching are two crucial challenges that remain unaddressed. To address the above issues, we propose a paradigm to utilize LLM to optimize the task definition, evolve the datasets, and strengthen our proposed customized Contextual Highlighting Event Causality Extraction framework (CHECE). Specifically in CHECE, we propose an Event Highlighter and an Event Concretization Module, guiding the model to represent the event by a higher-level cluster and consider its causal counterpart in event boundary prediction to deal with event boundary deviation. And we propose a Contextual Event Causality Matching mechanism, meanwhile, applying LLM to diversify the content templates to force the model to learn causality from context to targeting on causal event pair mismatching. Experimental results on two ECE datasets demonstrate the effectiveness of our method.
Luo, Kun Zhou, Tong Chen, Yubo Zhao, Jun Liu, Kang
Open Event Causality Extraction by the Assistance of LLM in Task Annotation, Dataset, and Method