打破知识的诅咒：为实时在线会议设计个性化术语支持
Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings
AI总结：
提出ParseJargon系统，利用用户画像和会话内反馈实现个性化术语识别，提升在线会议中跨学科听众的理解和参与度。
摘要：
跨学科交流常常受到专业语言（即术语）和不均衡背景知识的阻碍。语音转文本和大语言模型的最新进展使得在在线会议期间提供术语支持成为可能，但通用支持（即对每个人定义相同的术语）可能会用听众不需要的定义淹没他们。我们提出了ParseJargon，一个用于实时在线会议中个性化术语支持的系统。我们从一个初始原型开始，探索使用单句用户画像进行个性化。我们进行了一项对照研究，结果表明，与通用支持相比，即使这种最小程度的个性化也能增强听众的理解和参与度，因为术语识别更精确。根据参与者反馈的见解，我们改进了系统，采用了更先进的个性化技术，包括会话内用户反馈和基于便携式词汇表的画像。我们评估了这些技术如何进一步提高术语识别精度，使用对照研究中收集的数据来模拟随时间变化的个性化。我们还进行了延迟测试，并辅以轻量级部署，以分析系统的实时能力和可用性。
英文摘要：
Cross-disciplinary communication is often hindered by specialized language (i.e., jargon) and uneven background knowledge. Recent advances in speech-to-text and large language models make it possible to provide jargon support during online meetings, but generic support (i.e., defining the same terms for everyone) can overwhelm listeners with definitions they do not need. We present ParseJargon, a system for personalized jargon support in real-time online meetings. We begin with an initial prototype to probe the use of single-sentence user profiles for personalization. We conducted a controlled study and showed that even this minimal personalization enhanced listeners' comprehension and engagement over generic support because of more precise jargon identification. Guided by insights from participants' feedback, we refined the system with more advanced personalization techniques, including in-session user feedback and portable glossary-based profiles. We evaluated how these techniques can further improve jargon identification precision using data collected in the controlled study to simulate personalization over time. We also conducted a latency test, complemented by a lightweight deployment, to analyze the system's real-time capability and usability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
