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Existing works on coreference resolution suggest that task-specific modelsare necessary to achieve state-of-the-art performance. In this work, we presentcompelling evidence that such models are not necessary. We finetune apretrained seq2seq transformer to map an input document to a tagged sequenceencoding the coreference annotation. Despite the extreme simplicity, our modeloutperforms or closely matches the best coreference systems in the literatureon an array of datasets. We also propose an especially simple seq2seq approachthat generates only tagged spans rather than the spans interleaved with theoriginal text. Our analysis shows that the model size, the amount ofsupervision, and the choice of sequence representations are key factors inperformance.

Seq2seq is All You Need for Coreference Resolution