The limited generalization of coreference resolution (CR) models has been a
major bottleneck in the task's broad application. Prior work has identified
annotation differences, especially for mention detection, as one of the main
reasons for the generalization gap and proposed using additional annotated
target domain data. Rather than relying on this additional annotation, we
propose an alternative formulation of the CR task, Major Entity Identification
(MEI), where we: (a) assume the target entities to be specified in the input,
and (b) limit the task to only the frequent entities. Through extensive
experiments, we demonstrate that MEI models generalize well across domains on
multiple datasets with supervised models and LLM-based few-shot prompting.
Additionally, the MEI task fits the classification framework, which enables the
use of classification-based metrics that are more robust than the current CR
metrics. Finally, MEI is also of practical use as it allows a user to search
for all mentions of a particular entity or a group of entities of interest.

核心 ference 解析模型的有限泛化成为该任务广泛应用的主要瓶颈，通过对比不同的标注、使用额外的目标域数据，以及提出另一种 CR 任务的替代公式 ——Major Entity Identification（MEI），我们证明 MEI 模型在多个数据集上以监督模型和基于 LLM 的少量样本提示为基础，以良好的泛化性能适用于各种领域。此外，MEI 任务符合分类框架，使得可以使用比当前 CR 指标更稳健的基于分类的度量。最后，MEI 在实际应用中也很有用，因为它允许用户搜索特定实体或一组感兴趣的实体的所有提及。