@inproceedings{bourgonje-demberg-2024-generalizing,
    title = "Generalizing across Languages and Domains for Discourse Relation Classification",
    author = "Bourgonje, Peter  and
      Demberg, Vera",
    editor = "Kawahara, Tatsuya  and
      Demberg, Vera  and
      Ultes, Stefan  and
      Inoue, Koji  and
      Mehri, Shikib  and
      Howcroft, David  and
      Komatani, Kazunori",
    booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2024",
    address = "Kyoto, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.sigdial-1.47/",
    doi = "10.18653/v1/2024.sigdial-1.47",
    pages = "554--565",
    abstract = "The availability of corpora annotated for discourse relations is limited and discourse relation classification performance varies greatly depending on both language and domain. This is a problem for downstream applications that are intended for a language (i.e., not English) or a domain (i.e., not financial news) with comparatively low coverage for discourse annotations. In this paper, we experiment with a state-of-the-art model for discourse relation classification, originally developed for English, extend it to a multi-lingual setting (testing on Italian, Portuguese and Turkish), and employ a simple, yet effective method to mark out-of-domain training instances. By doing so, we aim to contribute to better generalization and more robust discourse relation classification performance across both language and domain."
}