@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://preview.aclanthology.org/ingest-acl/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."
}