@inproceedings{ju-etal-2025-dedisco,
    title = "{D}e{D}is{C}o at the {DISRPT} 2025 Shared Task: A System for Discourse Relation Classification",
    author = "Ju, Zhuoxuan  and
      Wu, Jingni  and
      Purushothama, Abhishek  and
      Zeldes, Amir",
    editor = "Braud, Chlo{\'e}  and
      Liu, Yang Janet  and
      Muller, Philippe  and
      Zeldes, Amir  and
      Li, Chuyuan",
    booktitle = "Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.disrpt-1.4/",
    doi = "10.18653/v1/2025.disrpt-1.4",
    pages = "48--62",
    ISBN = "979-8-89176-344-9",
    abstract = "This paper presents DeDisCo, Georgetown University{'}s entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results."
}