@inproceedings{li-ng-2022-end,
    title = "End-to-End Neural Discourse Deixis Resolution in Dialogue",
    author = "Li, Shengjie  and
      Ng, Vincent",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.emnlp-main.778/",
    doi = "10.18653/v1/2022.emnlp-main.778",
    pages = "11322--11334",
    abstract = "We adapt Lee et al.{'}s (2018) span-based entity coreference model to the task of end-to-end discourse deixis resolution in dialogue, specifically by proposing extensions to their model that exploit task-specific characteristics. The resulting model, dd-utt, achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task."
}