  Since the first end-to-end neural coreference resolution model was
introduced, many extensions to the model have been proposed, ranging from using
higher-order inference to directly optimizing evaluation metrics using
reinforcement learning. Despite improving the coreference resolution
performance by a large margin, these extensions add substantial extra
complexity to the original model. Motivated by this observation and the recent
advances in pre-trained Transformer language models, we propose a simple yet
effective baseline for coreference resolution. Even though our model is a
simplified version of the original neural coreference resolution model, it
achieves impressive performance, outperforming all recent extended works on the
public English OntoNotes benchmark. Our work provides evidence for the
necessity of carefully justifying the complexity of existing or newly proposed
models, as introducing a conceptual or practical simplification to an existing
model can still yield competitive results.
