Enhancing Discourse Coherence to Improve Cross-Document Event Coreference Resolution

Xinyu Chen, Sheng Xu, Peifeng Li, Qiaoming Zhu

Abstract: Cross-Document Event Coreference Resolution (CD-ECR) is a task of grouping event mentions across multiple documents that refer to the same real-world events. In contrast to within-document event mentions, which are linked by rich, coherent contexts, cross-document event mentions lack such contexts, making it challenging for the model to establish a connection between two event mentions in different documents. To address this issue, we propose a novel mechanism of enhancing discourse coherence to boost CD-ECR. Specifically, we introduce a new task, ECD-CoE (Event-oriented Cross-Document Coherence Enhancement), which selects coherent sentences that form a coherent text for two cross-document event mentions. We then use this coherent text to represent the event mentions and resolve coreferent events. Experimental results on both the ECB+ and GVC datasets indicate that our proposed method outperforms several state-of-the-art baselines.