@inproceedings{park-etal-2024-improving,
    title = "Improving Backchannel Prediction Leveraging Sequential and Attentive Context Awareness",
    author = "Park, Yo-Han  and
      Liermann, Wencke  and
      Choi, Yong-Seok  and
      Lee, Kong Joo",
    editor = "Graham, Yvette  and
      Purver, Matthew",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-eacl.118/",
    pages = "1689--1694",
    abstract = "Backchannels, which refer to short and often affirmative or empathetic responses from a listener during a conversation, play a crucial role in effective communication. In this paper, we introduce CABP(Context-Aware Backchannel Prediction), a sequential and attentive context approach aimed at enhancing backchannel prediction performance. Additionally, CABP leverages the pretrained wav2vec model for encoding audio signal. Experimental results show that CABP performs better than context-free models, with performance improvements of 1.3{\%} and 1.8{\%} in Korean and English datasets, respectively. Furthermore, when utilizing the pretrained wav2vec model, CABP consistently demonstrates the best performance, achieving performance improvements of 4.4{\%} and 3.1{\%} in Korean and English datasets."
}