Text-Based Detection of On-Hold Scripts in Contact Center Calls
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916322519023616 |
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| author | Galimzianov, Dmitrii Vyshegorodtsev, Viacheslav |
| author_facet | Galimzianov, Dmitrii Vyshegorodtsev, Viacheslav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09849 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Text-Based Detection of On-Hold Scripts in Contact Center Calls Galimzianov, Dmitrii Vyshegorodtsev, Viacheslav Computation and Language Machine Learning 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. |
| title | Text-Based Detection of On-Hold Scripts in Contact Center Calls |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2407.09849 |