Efficient Document-level Event Relation Extraction

AbstractEvent Relation Extraction (ERE) predicts temporal and causal relationships between events, playing a crucial role in constructing comprehensive event knowledge graphs. However, existing approaches based on pairwise comparisons often suffer from computational inefficiency, particularly at the document level, due to the quadratic operations required. Additionally, the predominance of unrelated events also leads to largely skewed data distributions. In this paper, we propose an innovative two-stage framework to tackle the challenges, consisting of a retriever to identify the related event pairs and a cross-encoder to classify the relationships between the retrieved pairs. Evaluations across representative benchmarks demonstrate our approach achieves better efficiency and significantly better performance. We also investigate leveraging event coreference chains for ERE and demonstrate their effectiveness.