  Event Causality Extraction (ECE) aims at extracting causal event pairs from
texts. Despite ChatGPT's recent success, fine-tuning small models remains the
best approach for the ECE task. However, existing fine-tuning based ECE methods
cannot address all three key challenges in ECE simultaneously: 1) Complex
Causality Extraction, where multiple causal-effect pairs occur within a single
sentence; 2) Subtask~ Interaction, which involves modeling the mutual
dependence between the two subtasks of ECE, i.e., extracting events and
identifying the causal relationship between extracted events; and 3) Knowledge
Fusion, which requires effectively fusing the knowledge in two modalities,
i.e., the expressive pretrained language models and the structured knowledge
graphs. In this paper, we propose a unified ECE framework (UniCE to address all
three issues in ECE simultaneously. Specifically, we design a subtask
interaction mechanism to enable mutual interaction between the two ECE
subtasks. Besides, we design a knowledge fusion mechanism to fuse knowledge in
the two modalities. Furthermore, we employ separate decoders for each subtask
to facilitate complex causality extraction. Experiments on three benchmark
datasets demonstrate that our method achieves state-of-the-art performance and
outperforms ChatGPT with a margin of at least 30% F1-score. More importantly,
our model can also be used to effectively improve the ECE performance of
ChatGPT via in-context learning.
