Papers › Coreference Resolution without Span Representations

Coreference Resolution without Span Representations

2 Jan 2021ACL 2021 5arXiv:2101.00434archive 2025-07-28

Yuval Kirstain, Ori Ram, Omer Levy

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.

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