@inproceedings{kikteva-etal-2024-question,
    title = "Question Type Prediction in Natural Debate",
    author = "Kikteva, Zlata  and
      Trautsch, Alexander  and
      Herbold, Steffen  and
      Hautli-Janisz, Annette",
    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_wac_2008/2024.sigdial-1.53/",
    doi = "10.18653/v1/2024.sigdial-1.53",
    pages = "624--630",
    abstract = "In spontaneous natural debate, questions play a variety of crucial roles: they allow speakers to introduce new topics, seek other speakers' opinions or indeed confront them. A three-class question typology has previously been demonstrated to effectively capture details pertaining to the nature of questions and the different functions associated with them in a debate setting. We adopt this classification and investigate the performance of several machine learning approaches on this task by incorporating various sets of lexical, dialogical and argumentative features. We find that BERT demonstrates the best performance on the task, followed by a Random Forest model enriched with pragmatic features."
}