@inproceedings{lambropoulos-ishihara-2024-towards,
    title = "Towards an Implementation of {R}hetorical {S}tructure {T}heory in Discourse Coherence Modelling",
    author = "Lambropoulos, Michael  and
      Ishihara, Shunichi",
    editor = "Baldwin, Tim  and
      Rodr{\'i}guez M{\'e}ndez, Sergio Jos{\'e}  and
      Kuo, Nicholas",
    booktitle = "Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2024",
    address = "Canberra, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.alta-1.1/",
    pages = "1--11",
    abstract = "In this paper, we combine the discourse coherence principles of Elementary Discourse Unit segmentation and Rhetorical Structure Theory parsing to construct meaningful graph-based text representations. We then evaluate a Graph Convolutional Network and a Graph Attention Network on these representations. Our results establish a new benchmark in F1-score assessment for discourse coherence modelling while also showing that Graph Convolutional Network models are generally more computationally efficient and provide superior accuracy."
}