Conversational Rubert for Detecting Competitive Interruptions in ASR-Transcribed Dialogues

TL;DR

This paper presents a text-based model using Conversational RuBERT to classify interruptions in Russian customer support dialogues, aiming to improve automatic monitoring of conversational quality.

Contribution

The study introduces a novel application of fine-tuned Conversational RuBERT for classifying cooperative and competitive interruptions in ASR-transcribed dialogues.

Findings

Model achieved high classification accuracy

Effective hyperparameter optimization was performed

Potential for integration into automatic monitoring systems

Abstract

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.