Event relation extraction (ERE) is a critical and fundamental challenge for
natural language processing. Existing work mainly focuses on directly modeling
the entire document, which cannot effectively handle long-range dependencies
and information redundancy. To address these issues, we propose a cluster-aware
compression method for improving event relation extraction (TacoERE), which
explores a compression-then-extraction paradigm. Specifically, we first
introduce document clustering for modeling event dependencies. It splits the
document into intra- and inter-clusters, where intra-clusters aim to enhance
the relations within the same cluster, while inter-clusters attempt to model
the related events at arbitrary distances. Secondly, we utilize cluster
summarization to simplify and highlight important text content of clusters for
mitigating information redundancy and event distance. We have conducted
extensive experiments on both pre-trained language models, such as RoBERTa, and
large language models, such as ChatGPT and GPT-4, on three ERE datasets, i.e.,
MAVEN-ERE, EventStoryLine and HiEve. Experimental results demonstrate that
TacoERE is an effective method for ERE.

我们提出了一种关于事件关系抽取的聚类感知压缩方法 (TacoERE)，该方法通过聚类和压缩策略对文档进行建模，用于提高事件关系抽取的效果。实验结果表明 TacoERE 是一种有效的 ERE 方法。