Conversational Rubert for Detecting Competitive Interruptions in ASR-Transcribed Dialogues
This work addresses monitoring needs in call centers for customer and agent satisfaction, but it is incremental as it applies an existing method to a new dataset.
The study tackled the problem of automatically classifying competitive interruptions in ASR-transcribed Russian customer support dialogues by fine-tuning Conversational RuBERT on an in-house dataset, achieving good performance.
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.