@inproceedings{sato-etal-2025-partial,
    title = "Is Partial Linguistic Information Sufficient for Discourse Connective Disambiguation? A Case Study of Concession",
    author = "Sato, Takuma  and
      Kubota, Ai  and
      Mineshima, Koji",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.acl-srw.71/",
    pages = "977--990",
    ISBN = "979-8-89176-254-1",
    abstract = "Discourse relations are sometimes explicitly conveyed by specific connectives.However, some connectives can signal multiple discourse relations; in such cases, disambiguation is necessary to determine which relation is intended.This task is known as *discourse connective disambiguation* (Pitler and Nenkova, 2009), and particular attention is often given to connectives that can convey both *concession* and other relations (e.g., *synchronous*).In this study, we conducted experiments to analyze which linguistic features play an important role in the disambiguation of polysemous connectives in Japanese.A neural language model (BERT) was fine-tuned using inputs from which specific linguistic features (e.g., word order, specific lexicon, etc.) had been removed.We analyzed which linguistic features affect disambiguation by comparing the model{'}s performance.Our results show that even after performing drastic removal, such as deleting one of the two arguments that constitute the discourse relation, the model{'}s performance remained relatively robust.However, the removal of certain lexical items or words belonging to specific lexical categories significantly degraded disambiguation performance, highlighting their importance in identifying the intended discourse relation."
}