Coreference Resolution without Span Representations

Understand

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.