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
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.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表
Yifan Song University of Illinois at Urbana-Champaign, Wenxuan Wendy Shi California Polytechnic State University, Pomona, Brian Bailey, Tal August University of Illinois at Urbana-Champaign
Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or data-driven clustering rather than theory-grounded taxonomies. We operationalize a taxonomy of eight communication roles grounded in education literature and annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project. We evaluate whether LLMs can approximate expert labels, enabling scalable, taxonomy-driven role annotation. Using these role labels, we characterize role dynamics over teams’ lifecycles, finding that different roles peak at different moments and that students enact a more diverse set of roles as projects progress. To evaluate the utility of our role constructs, we use them to predict peer recognition, outperforming lexical, conversational, and LLM-prompting baselines. To assess generalizability beyond the educational context, we apply the same role constructs to a public dataset (DeliData) to predict team performance improvement after deliberation, again exceeding prior performance.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表