@inproceedings{kahardipraja-etal-2020-exploring,
    title = "Exploring Span Representations in Neural Coreference Resolution",
    author = "Kahardipraja, Patrick  and
      Vyshnevska, Olena  and
      Lo{\'a}iciga, Sharid",
    editor = "Braud, Chlo{\'e}  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Louis, Annie  and
      Strube, Michael",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.codi-1.4/",
    doi = "10.18653/v1/2020.codi-1.4",
    pages = "32--41",
    abstract = "In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference."
}