See also NLP Progress - Coreference Resolution.

• Stylianou & Vlahavas 2019 - A Neural Entity Coreference Resolution review
• Anaphora review: Sukthanker et al 2018 - Anaphora and coreference resolution: A review

• Neural Methods
  • Clark 2016 (Kevin Clark's preliminary work)
  • Clark & Manning 2016 - Improving Coreference Resolution by Learning Entity-Level Distributed Representations
  • Clark & Manning 2016 - Deep Reinforcement Learning for Mention-Ranking Coreference Models
  • Lee et al 2017 - End-to-end Neural Coreference Resolution
  • Lee et al 2018 - Higher-order Coreference Resolution with Coarse-to-fine Inference
  • Xu & Choi 2020 - Revealing the Myth of Higher-Order Inference in Coreference Resolution
  • Kirstain et al 2021 - Coreference Resolution without Span Representations
  • Otmazgin et al 2023 - LINGMESS: Linguistically Informed Multi Expert Scorers for Coreference Resolution
• Prompting Methods
  • Wu et al 2020 - CorefQA: Coreference Resolution as Query-based Span Prediction
  • Le & Ritter 2023 - Are Large Language Models Robust Zero-shot Coreference Resolvers? See their prompt in the appendix (Table 10)

• Overviews
  • Event Coreference Resolution: A Survey of Two Decades of Research
• Papers
  • Lee et al 2012 - Joint Entity and Event Coreference Resolution across Documents
  • Zheng et al 2020 - Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings

• CoNLL 2012 Shared Task
• Winobias dataset: paper
• GAP dataset:
• BUG dataset: Levy et al 2021 - Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation

Many papers use $B^3$ (B-cubed), MUC, CEAF, or an average of these three as the metric. The CoNLL-2011/2012 shared tasks (Pradhan et al 2011, Pradhan et al 2012) used an average. See also these papers:

• Moosavi & Strube 2016 - Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric (gives a good overview of previous metrics)