ParseJargon: Personalized Real-time Jargon Support in Online Meetings
Yifan Song University of Illinois at Urbana-Champaign, Wing Yee Au Fujitsu Research of America, Hon Yung Wong, Brian Bailey, Tal August University of Illinois at Urbana-Champaign
原文摘要
Effective interdisciplinary communication is frequently hindered by domain-specific terms. These terms, or jargon, are dependent on a listener’s background, and rarely do listeners seek explanations due to distraction and social concerns. To address these concerns, we built ParseJargon, an interactive LLM-powered system providing real-time personalized jargon support tailored to users’ individual backgrounds in online meetings. We first evaluated the effectiveness of personalization in a controlled setting with human participants. By comparing ParseJargon against baseline (no support) and general-purpose (non-personalized) conditions, we found that ParseJargon provided more precise jargon identification, and enhanced participants’ comprehension, engagement, and appreciation of colleagues’ work. We then evaluated the potential for using ParseJargon in real-time meetings through a latency test.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表