@inproceedings{omura-etal-2024-empirical,
    title = "An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition",
    author = "Omura, Kazumasa  and
      Cheng, Fei  and
      Kurohashi, Sadao",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2024.lrec-main.96/",
    pages = "1073--1085",
    abstract = "Implicit Discourse Relation Recognition (IDRR), which is the task of recognizing the semantic relation between given text spans that do not contain overt clues, is a long-standing and challenging problem. In particular, the paucity of training data for some error-prone discourse relations makes the problem even more challenging. To address this issue, we propose a method of generating synthetic data for IDRR using a large language model. The proposed method is summarized as two folds: extraction of confusing discourse relation pairs based on false negative rate and synthesis of data focused on the confusion. The key points of our proposed method are utilizing a confusion matrix and adopting two-stage prompting to obtain effective synthetic data. According to the proposed method, we generated synthetic data several times larger than training examples for some error-prone discourse relations and incorporated it into training. As a result of experiments, we achieved state-of-the-art macro-F1 performance thanks to the synthetic data without sacrificing micro-F1 performance and demonstrated its positive effects especially on recognizing some infrequent discourse relations."
}