Existing cross-document event coreference resolution models, which either
compute mention similarity directly or enhance mention representation by
extracting event arguments (such as location, time, agent, and patient),
lacking the ability to utilize document-level information. As a result, they
struggle to capture long-distance dependencies. This shortcoming leads to their
underwhelming performance in determining coreference for the events where their
argument information relies on long-distance dependencies. In light of these
limitations, we propose the construction of document-level Rhetorical Structure
Theory (RST) trees and cross-document Lexical Chains to model the structural
and semantic information of documents. Subsequently, cross-document
heterogeneous graphs are constructed and GAT is utilized to learn the
representations of events. Finally, a pair scorer calculates the similarity
between each pair of events and co-referred events can be recognized using
standard clustering algorithm. Additionally, as the existing cross-document
event coreference datasets are limited to English, we have developed a
large-scale Chinese cross-document event coreference dataset to fill this gap,
which comprises 53,066 event mentions and 4,476 clusters. After applying our
model on the English and Chinese datasets respectively, it outperforms all
baselines by large margins.

通过构建文档级的修辞结构理论（Rhetorical Structure Theory, RST）树和跨文档词汇链，再利用异构图和 GAT 模型学习事件的表示，最后使用标准聚类算法识别共指事件，我们提出了针对现有的完全依赖于长距离依赖关系的事件关联模型不足的解决方案，并且在英文和中文数据集上都取得了显著的突破，超过了所有基准模型。