@inproceedings{guan-etal-2025-enhancing,
    title = "Enhancing the Automatic Classification of Metadiscourse in Low-Proficiency Learners' Spoken and Written {E}nglish Texts Using {XLN}et",
    author = "Guan, Wenwen  and
      Alta, Marijn  and
      Bloem, Jelke",
    editor = "Strube, Michael  and
      Braud, Chloe  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Loaiciga, Sharid  and
      Zeldes, Amir  and
      Li, Chuyuan",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-11/2025.codi-1.3/",
    doi = "10.18653/v1/2025.codi-1.3",
    pages = "27--41",
    ISBN = "979-8-89176-343-2",
    abstract = "This study aims to enhance the automatic identification and classification of metadiscourse markers in English texts, evaluating various large language models for the purpose. Metadiscourse is a commonly used rhetorical strategy in both written and spoken language to guide addressees through discourse. Due to its linguistic complexity and dependency on the context, automated metadiscourse classification is challenging. With a hypothesis that LLMs may handle complicated tasks more effectively than supervised machine learning approaches, we tune and evaluate seven encoder language models on the task using a dataset totalling 575,541 tokens and annotated with 24 labels. The results show a clear improvement over supervised machine learning approaches as well as an untuned Llama3.3-70B-Instruct baseline, with XLNet-large achieving an accuracy and F1-score of 0.91 and 0.93, respectively. However, four less frequent categories record F-scores below 0.5, highlighting the need for more balanced data representation."
}