@inproceedings{lu-ng-2020-conundrums,
    title = "Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art",
    author = "Lu, Jing  and
      Ng, Vincent",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.emnlp-main.536/",
    doi = "10.18653/v1/2020.emnlp-main.536",
    pages = "6620--6631",
    abstract = "Despite the significant progress on entity coreference resolution observed in recent years, there is a general lack of understanding of what has been improved. We present an empirical analysis of state-of-the-art resolvers with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research."
}