Speaker Role Identification in Call Centre Dialogues: Leveraging Opening Sentences and Large Language Models
Minh-Quoc Nghiem, Nichola Roberts, Dmitry Sityaev · 2023
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.