Scaling Within Document Coreference to Long Texts

State of the art end-to-end coreference resolution models use expensive span representations and antecedent prediction mechanisms. These approaches are expensive both in terms of their memory requirements as well as compute time, and are particularly ill-suited for long documents. In this paper, we propose an approximation to end-to-end models which scales gracefully to documents of any length. Replacing span representations with token representations, we reduce the time/memory complexity via token windows and nearest neighbor sparsification methods for more efficient antecedent prediction. We show our approach's resulting reduction of training and inference time compared to state-of-the-art methods with only a minimal loss in accuracy.