Text-Based Detection of On-Hold Scripts in Contact Center Calls
<|reference_start|>Text-Based Detection of On-Hold Scripts in Contact Center Calls: Average hold time is a concern for call centers because it affects customer satisfaction. Contact centers should instruct their agents to use special on-hold scripts to maintain positive interactions with clients. This study presents a natural language processing model that detects on-hold phrases in customer service calls transcribed by automatic speech recognition technology. The task of finding hold scripts in dialogue was formulated as a multiclass text classification problem with three mutually exclusive classes: scripts for putting a client on hold, scripts for returning to a client, and phrases irrelevant to on-hold scripts. We collected an in-house dataset of calls and labeled each dialogue turn in each call. We fine-tuned RuBERT on the dataset by exploring various hyperparameter sets and achieved high model performance. The developed model can help agent monitoring by providing a way to check whether an agent follows predefined on-hold scripts.<|reference_end|>
@article{galimzianov2024text-based,
    title={Text-Based Detection of On-Hold Scripts in Contact Center Calls},
    author={Dmitrii Galimzianov, Viacheslav Vyshegorodtsev},
    journal={Machine Learning and Applications: An International Journal
  (MLAIJ) Vol.11, No.2, June 2024},
    year={2024},
    doi={10.5121/mlaij.2024.11201},
    archivePrefix={arXiv},
    eprint={2407.09849},
    primaryClass={cs.CL cs.LG}
}