ParseJargon: Personalized Real-Time Jargon Support System for Online Meetings
Researchers have developed ParseJargon, a system that provides personalized definitions of specialized terminology during online meetings using speech-to-text and large language models. The system tailors jargon explanations to individual participants' knowledge levels rather than showing generic definitions to everyone. A controlled study demonstrated that personalized jargon support improved listener comprehension and engagement compared to one-size-fits-all approaches.
ParseJargon addresses a common challenge in cross-disciplinary communication: specialized language that can confuse participants with different background knowledge. The system leverages recent advances in speech-to-text technology and large language models to identify jargon in real-time and deliver personalized definitions based on individual user profiles. The research team tested an initial prototype using single-sentence user profiles and found it enhanced comprehension and engagement through more precise jargon identification. Building on participant feedback, they refined the system with advanced personalization techniques including in-session user feedback and portable glossary-based profiles. The researchers evaluated these improvements using data from their controlled study and conducted latency testing to ensure real-time capability, demonstrating the system's practical viability for actual deployment.
What's missing
The paper does not specify the size of the controlled study participant pool, the range of disciplines tested, or comparative performance metrics against other jargon support approaches. Additionally, the study's limitations regarding generalization to different meeting contexts and languages are not detailed in the abstract.
What different sources said
- arXiv cs.CLCenter
Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings
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