@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Speaker Role Identification in Call Centre Dialogues: Leveraging Opening Sentences and Large Language Models
%A Nghiem, Minh-Quoc
%A Roberts, Nichola
%A Sityaev, Dmitry
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F nghiem-etal-2023-speaker
%X 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.
%R 10.18653/v1/2023.sigdial-1.35
%U https://aclanthology.org/2023.sigdial-1.35
%U https://doi.org/10.18653/v1/2023.sigdial-1.35
%P 388-392
Markdown (Informal)
[Speaker Role Identification in Call Centre Dialogues: Leveraging Opening Sentences and Large Language Models](https://aclanthology.org/2023.sigdial-1.35) (Nghiem et al., SIGDIAL 2023)
ACL