  We investigate modeling coreference resolution under a fixed memory
constraint by extending an incremental clustering algorithm to utilize
contextualized encoders and neural components. Given a new sentence, our
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
forgotten; 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.
