# Conversational Rubert for Detecting Competitive Interruptions in ASR-Transcribed Dialogues

arXiv: https://arxiv.org/abs/2407.14940 · HTML: https://mlsanity.com/paper/2407.14940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detection](https://mlsanity.com/paper/1910.11646/) (2019)
- [Oh, Jeez! or Uh-huh? A Listener-aware Backchannel Predictor on ASR Transcriptions](https://mlsanity.com/paper/2304.04478/) (2023)
- [End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection](https://mlsanity.com/paper/2106.04078/) (2021)
- [Improving Meeting Inclusiveness using Speech Interruption Analysis](https://mlsanity.com/paper/2304.00658/) (2023)
- [Text-Based Detection of On-Hold Scripts in Contact Center Calls](https://mlsanity.com/paper/2407.09849/) (2024)

## Abstract (arXiv)

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.