Before revision:  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 approach is end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity's representations before being URLotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3\% relative loss in F1 on OntoNotes 5.0 .