Summary of Conversational Rubert For Detecting Competitive Interruptions in Asr-transcribed Dialogues, by Dmitrii Galimzianov et al.
Conversational Rubert for Detecting Competitive Interruptions in ASR-Transcribed Dialogues
by Dmitrii Galimzianov, Viacheslav Vyshegorodtsev
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study proposes a text-based interruption classification model for detecting cooperative and competitive interruptions in customer support telephone dialogues in Russian. The model is developed by fine-tuning Conversational RuBERT on an in-house dataset of ASR-transcribed dialogues, and the results show promising performance. The proposed system can be used to improve monitoring systems in call centers, enhancing customer satisfaction and agent efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a special computer program that can identify when someone interrupts another person talking. This is useful for businesses like call centers where it’s important to keep track of how customers are being helped and how the people helping them are doing their job. The program works by studying conversations between customers and customer service representatives in Russia, and then using what it learns to categorize interruptions as either helpful or not helpful. This could help companies make sure their customer service is top-notch. |
Keywords
* Artificial intelligence * Classification * Fine tuning
