基于文本的呼叫中心通话中等待话术检测
Text-Based Detection of On-Hold Scripts in Contact Center Calls
AI总结：
针对呼叫中心等待时长影响满意度的问题，本研究将等待话术检测定义为三分类文本任务，基于内部标注数据集微调RuBERT模型，实现高准确率检测，可辅助监控坐席是否遵循预设等待话术。
摘要：
平均等待时长是呼叫中心关注的问题，因其会影响客户满意度。呼叫中心应指导坐席使用专门的等待话术，以维持与客户的良性互动。本研究提出一种自然语言处理模型，用于检测经自动语音识别技术转写的客服通话中的等待相关表述。我们将对话中查找等待话术的任务定义为三分类互斥的多类文本分类问题，三个类别分别为：告知客户进入等待的话术、告知客户返回通话的话术，以及与等待话术无关的表述。我们收集了内部通话数据集，并对每通通话的每个对话轮次进行标注。我们通过探索多组超参数，在该数据集上微调RuBERT模型，取得了较高的模型性能。所开发的模型可用于检查坐席是否遵循预设的等待话术，从而辅助坐席监控工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
