@inproceedings{yung-etal-2025-synthetic,
    title = "Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition",
    author = "Yung, Frances  and
      Suresh, Varsha  and
      Reza, Zaynab  and
      Ahmad, Mansoor  and
      Demberg, Vera",
    editor = "B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Lef{\`e}vre, Fabrice  and
      Asher, Nicholas  and
      Kim, Seokhwan  and
      Merlin, Teva",
    booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2025",
    address = "Avignon, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-orcids-2023-acl/2025.sigdial-1.13/",
    pages = "172--182",
    abstract = "Implicit discourse relation recognition (IDRR) {--} the task of identifying the implicit coherence relation between two text spans {--} requires deep semantic understanding. Recent studies have shown that zero-/few-shot approaches significantly lag behind supervised models. However, LLMs may be useful for synthetic data augmentation, where LLMs generate a second argument following a specified coherence relation. We applied this approach in a cross-domain setting, generating discourse continuations using unlabelled target-domain data to adapt a base model which was trained on source-domain labelled data. Evaluations conducted on a large-scale test set revealed that different variations of the approach did not result in any significant improvements. We conclude that LLMs often fail to generate useful samples for IDRR, and emphasize the importance of considering both statistical significance and comparability when evaluating IDRR models."
}