@inproceedings{fu-2025-cross,
    title = "Cross-Framework Generalizable Discourse Relation Classification Through Cognitive Dimensions",
    author = "Fu, Yingxue",
    editor = "Kuribayashi, Tatsuki  and
      Rambelli, Giulia  and
      Takmaz, Ece  and
      Wicke, Philipp  and
      Li, Jixing  and
      Oh, Byung-Doh",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-06/2025.cmcl-1.16/",
    doi = "10.18653/v1/2025.cmcl-1.16",
    pages = "104--134",
    ISBN = "979-8-89176-227-5",
    abstract = "Existing discourse corpora annotated under different frameworks adopt distinct but somewhat related taxonomies of relations. How to integrate discourse frameworks has been an open research question. Previous studies on this topic are mainly theoretical, although such research is typically performed with the hope of benefiting computational applications. In this paper, we show how the proposal by Sanders et al. (2018) based on the Cognitive approach to Coherence Relations (CCR) (Sanders et al.,1992, 1993) can be used effectively to facilitate cross-framework discourse relation (DR) classification. To address the challenges of using predicted UDims for DR classification, we adopt the Bayesian learning framework based on Monte Carlo dropout (Gal and Ghahramani, 2016) to obtain more robust predictions. Data augmentation enabled by our proposed method yields strong performance (55.75 for RST and 55.01 for PDTB implicit DR classification in macro-averaged F1). We compare four model designs and analyze the experimental results from different perspectives. Our study shows an effective and cross-framework generalizable approach for DR classification, filling a gap in existing studies."
}