AQuA:利用视觉锚点在软件教程视频中自动答题

ArXiv Pub Date : 2024-03-08 DOI:10.1145/3613904.3642752
Saelyne Yang, Jo Vermeulen, G. Fitzmaurice, Justin Matejka
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引用次数: 0

摘要

教程视频是学习功能丰富的软件的常用帮助资源。然而,要快速回答有关教程视频的问题却很困难。我们提出了一种自动回复教程问题的方法。通过分析 5944 条视频评论中的 633 个问题,我们确定了不同的问题类型,并观察到用户经常在问题中描述视频的部分内容。然后,我们让参与者(24 人)观看教程视频并提问,同时在视频中标注相关的视觉锚点。大多数视觉锚点指的是用户界面元素和应用程序工作区。基于这些见解,我们建立了 AQuA,这是一个通过视觉锚点为问题生成有用答案的管道。我们为 Fusion 360 演示了这一功能,表明我们可以识别视觉锚点中的用户界面元素,并使用 GPT-4 生成带有视觉信息和软件文档的答案。一项评估研究(N=16)表明,与基线方法相比,我们的方法能提供更好的答案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AQuA: Automated Question-Answering in Software Tutorial Videos with Visual Anchors
Tutorial videos are a popular help source for learning feature-rich software. However, getting quick answers to questions about tutorial videos is difficult. We present an automated approach for responding to tutorial questions. By analyzing 633 questions found in 5,944 video comments, we identified different question types and observed that users frequently described parts of the video in questions. We then asked participants (N=24) to watch tutorial videos and ask questions while annotating the video with relevant visual anchors. Most visual anchors referred to UI elements and the application workspace. Based on these insights, we built AQuA, a pipeline that generates useful answers to questions with visual anchors. We demonstrate this for Fusion 360, showing that we can recognize UI elements in visual anchors and generate answers using GPT-4 augmented with that visual information and software documentation. An evaluation study (N=16) demonstrates that our approach provides better answers than baseline methods.
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