@inproceedings{he-etal-2022-evaluating,
    title = "Evaluating Discourse Cohesion in Pre-trained Language Models",
    author = "He, Jie  and
      Long, Wanqiu  and
      Xiong, Deyi",
    editor = "Braud, Chloe  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Loaiciga, Sharid  and
      Strube, Michael  and
      Zeldes, Amir",
    booktitle = "Proceedings of the 3rd Workshop on Computational Approaches to Discourse",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea and Online",
    publisher = "International Conference on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.codi-1.4/",
    pages = "28--34",
    abstract = "Large pre-trained neural models have achieved remarkable success in natural language process (NLP), inspiring a growing body of research analyzing their ability from different aspects. In this paper, we propose a test suite to evaluate the cohesive ability of pre-trained language models. The test suite contains multiple cohesion phenomena between adjacent and non-adjacent sentences. We try to compare different pre-trained language models on these phenomena and analyze the experimental results,hoping more attention can be given to discourse cohesion in the future. The built discourse cohesion test suite will be publicly available at \url{https://github.com/probe2/discourse_cohesion}."
}