@inproceedings{song-etal-2026-parsejargon,
title = "{P}arse{J}argon: Personalized Real-time Jargon Support in Online Meetings",
author = "Song, Yifan and
Au, Wing Yee and
Wong, Hon Yung and
Bailey, Brian and
August, Tal",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.61/",
pages = "615--625",
ISBN = "979-8-89176-392-0",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T ParseJargon: Personalized Real-time Jargon Support in Online Meetings
%A Song, Yifan
%A Au, Wing Yee
%A Wong, Hon Yung
%A Bailey, Brian
%A August, Tal
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F song-etal-2026-parsejargon
%X 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.
%U https://aclanthology.org/2026.acl-demo.61/
%P 615-625
Markdown (Informal)
[ParseJargon: Personalized Real-time Jargon Support in Online Meetings](https://aclanthology.org/2026.acl-demo.61/) (Song et al., ACL 2026)
ACL
- Yifan Song, Wing Yee Au, Hon Yung Wong, Brian Bailey, and Tal August. 2026. ParseJargon: Personalized Real-time Jargon Support in Online Meetings. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 615–625, San Diego, California, United States. Association for Computational Linguistics.