@inproceedings{liermann-etal-2023-dialogue,
    title = "Dialogue Act-Aided Backchannel Prediction Using Multi-Task Learning",
    author = "Liermann, Wencke  and
      Park, Yo-Han  and
      Choi, Yong-Seok  and
      Lee, Kong",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.findings-emnlp.1006/",
    doi = "10.18653/v1/2023.findings-emnlp.1006",
    pages = "15073--15079",
    abstract = "Produced in the form of small injections such as ``Yeah!'' or ``Uh-Huh'' by listeners in a conversation, supportive verbal feedback (i.e., backchanneling) is essential for natural dialogue. Highlighting its tight relation to speaker intent and utterance type, we propose a multi-task learning approach that learns textual representations for the task of backchannel prediction in tandem with dialogue act classification. We demonstrate the effectiveness of our approach by improving the prediction of specific backchannels like ``Yeah'' or ``Really?'' by up to 2.0{\%} in F1. Additionally, whereas previous models relied on well-established methods to extract audio features, we further pre-train the audio encoder in a self-supervised fashion using voice activity projection. This leads to additional gains of 1.4{\%} in weighted F1."
}