  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.
