Before revision:  Our algorithm creates explicit representations for each entity, where given a new sentence, spans are proposed and subsequently scored against each entity representation, leading to emergent clusters. Our approach is end-to-end trainable and can be used to transform existing models, leading to an asymptotic reduction in memory usage while remaining competitive on task performance, which allows for more efficient use of computational resources for short documents, and making coreference more feasible across very long document contexts .
After revision:  Our algorithm creates explicit representations for each entity, where given a new sentence, our end-to-end trainable and can be used to transform existing models, leading to an asymptotic reduction in memory usage while remaining competitive on task performance, which allows for more efficient use of computational resources for short documents, and making coreference more feasible across very long document contexts .