  The introduction of pretrained language models has reduced many complex
task-specific NLP models to simple lightweight layers. An exception to this
trend is coreference resolution, where a sophisticated task-specific model is
appended to a pretrained transformer encoder. While highly effective, the model
has a very large memory footprint -- primarily due to dynamically-constructed
span and span-pair representations -- which hinders the processing of complete
documents and the ability to train on multiple instances in a single batch. We
introduce a lightweight end-to-end coreference model that removes the
dependency on span representations, handcrafted features, and heuristics. Our
model performs competitively with the current standard model, while being
simpler and more efficient.
