用于检测ASR转录对话中竞争性打断的Conversational RuBERT
Conversational Rubert for Detecting Competitive Interruptions in ASR-Transcribed Dialogues
AI总结：
本研究针对ASR转录的俄语客户支持电话对话，通过微调Conversational RuBERT开发了一种基于文本的打断分类模型，能有效区分合作性与竞争性打断，可用于呼叫中心的自动监控。
摘要：
当听者在当前说话者结束讲话前开始发言时，对话中就会发生打断。打断可大致分为两类：合作性打断（听者想支持说话者）和竞争性打断（听者试图违背说话者意愿夺取对话控制权）。自动分类打断的系统可用于呼叫中心，特别是客户满意度监控和坐席监控任务。本研究通过准备一个包含俄语ASR转录的客户支持电话对话的内部数据集，开发了一个基于文本的打断分类模型。我们在数据集上微调了Conversational RuBERT并优化了超参数，模型表现良好。经过进一步改进，该模型可应用于自动监控系统。
英文摘要：
Interruption in a dialogue occurs when the listener begins their speech before the current speaker finishes speaking. Interruptions can be broadly divided into two groups: cooperative (when the listener wants to support the speaker), and competitive (when the listener tries to take control of the conversation against the speaker's will). A system that automatically classifies interruptions can be used in call centers, specifically in the tasks of customer satisfaction monitoring and agent monitoring. In this study, we developed a text-based interruption classification model by preparing an in-house dataset consisting of ASR-transcribed customer support telephone dialogues in Russian. We fine-tuned Conversational RuBERT on our dataset and optimized hyperparameters, and the model performed well. With further improvements, the proposed model can be applied to automatic monitoring systems.
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。
