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

• 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
• Le & Ritter 2023 - Are Large Language Models Robust Zero-shot Coreference Resolvers?

• 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)