Event Coreference Resolution (ECR) is the task of clustering event mentions
that refer to the same real-world event. Despite significant advancements, ECR
research faces two main challenges: limited generalizability across domains due
to narrow dataset evaluations, and difficulties in comparing models within
diverse ECR pipelines. To address these issues, we develop EasyECR, the first
open-source library designed to standardize data structures and abstract ECR
pipelines for easy implementation and fair evaluation. More specifically,
EasyECR integrates seven representative pipelines and ten popular benchmark
datasets, enabling model evaluations on various datasets and promoting the
development of robust ECR pipelines. By conducting extensive evaluation via our
EasyECR, we find that, \lowercase\expandafter{\romannumeral1}) the
representative ECR pipelines cannot generalize across multiple datasets, hence
evaluating ECR pipelines on multiple datasets is necessary,
\lowercase\expandafter{\romannumeral2}) all models in ECR pipelines have a
great effect on pipeline performance, therefore, when one model in ECR
pipelines are compared, it is essential to ensure that the other models remain
consistent. Additionally, reproducing ECR results is not trivial, and the
developed library can help reduce this discrepancy. The experimental results
provide valuable baselines for future research.

Event Coreference Resolution (ECR) is addressed through the development of EasyECR, an open-source library that standardizes data structures, abstracts ECR pipelines, integrates representative pipelines and benchmark datasets, and enables fair evaluation, revealing the need for evaluating ECR pipelines on multiple datasets and ensuring consistency when comparing models within the pipelines.