@inproceedings{nghiem-etal-2023-speaker,
    title = "Speaker Role Identification in Call Centre Dialogues: Leveraging Opening Sentences and Large Language Models",
    author = "Nghiem, Minh-Quoc  and
      Roberts, Nichola  and
      Sityaev, Dmitry",
    editor = "Stoyanchev, Svetlana  and
      Joty, Shafiq  and
      Schlangen, David  and
      Dusek, Ondrej  and
      Kennington, Casey  and
      Alikhani, Malihe",
    booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2023",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.sigdial-1.35/",
    doi = "10.18653/v1/2023.sigdial-1.35",
    pages = "388--392",
    abstract = "This paper addresses the task of speaker role identification in call centre dialogues, focusing on distinguishing between the customer and the agent. We propose a text-based approach that utilises the identification of the agent`s opening sentence as a key feature for role classification. The opening sentence is identified using a model trained through active learning. By combining this information with a large language model, we accurately classify the speaker roles. The proposed approach is evaluated on a dataset of call centre dialogues and achieves 93.61{\%} accuracy. This work contributes to the field by providing an effective solution for speaker role identification in call centre settings, with potential applications in interaction analysis and information retrieval."
}