Coreference Resolution through a seq2seq Transition-Based System

@article{Bohnet2022CoreferenceRT,
  title={Coreference Resolution through a seq2seq Transition-Based System},
  author={Bernd Bohnet and Chris Alberti and Michael Collins},
  journal={Transactions of the Association for Computational Linguistics},
  year={2022},
  volume={11},
  pages={212-226}
}
Abstract Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and links jointly. We implement the coreference system as a transition system and use multilingual T5 as an underlying language model. We obtain state-of-the-art accuracy on the CoNLL-2012 datasets with 83.3 F1-score for English (a 2.3 higher… 
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