HelpViz: Automatic Generation of Contextual Visual Mobile Tutorials from Text-Based Instructions
Mingyuan Zhong, Gang Li, Peggy Chi, Yang Li
摘要
We present HelpViz, a tool for generating contextual visual mobile tutorials from text-based instructions that are abundant on the web. HelpViz transforms text instructions to graphical tutorials in batch, by extracting a sequence of actions from each text instruction through an instruction parsing model, and executing the extracted actions on a simulation infrastructure that manages an array of Android emulators. The automatic execution of each instruction produces a set of graphical and structural assets, including images, videos, and metadata such as clicked elements for each step. HelpViz then synthesizes a tutorial by combining parsed text instructions with the generated assets, and contextualizes the tutorial to user interaction by tracking the user's progress and highlighting the next step. Our experiments with HelpViz indicate that our pipeline improved tutorial execution robustness and that participants preferred tutorials generated by HelpViz over text-based instructions. HelpViz promises a cost-effective approach for generating contextual visual tutorials for mobile interaction at scale.
• Human-centered computing → Interactive systems and tools.
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引用它的顶会 Paper6
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它引用的顶会 Paper5
- Mapping Natural Language Instructions to Mobile UI Action SequencesYang Li, Jiacong He, Xin Zhou, Yuan Zhang 等ACL 2020 · 被引用 75 次
- Automatic Generation of Two-Level Hierarchical Tutorials from Instructional Makeup VideosAnh Truong, Peggy Chi, David Salesin, Irfan Essa 等CHI 2021 · 被引用 57 次
- Generating Audio-Visual Slideshows from Text Articles Using Word ConcretenessMackenzie Leake, Hijung Valentina Shin, Joy O. Kim, Maneesh AgrawalaCHI 2020 · 被引用 36 次
- Composing Flexibly-Organized Step-by-Step Tutorials from Linked Source Code, Snippets, and OutputsAndrew Head, Jason Jiang, James Smith, Marti A. Hearst 等CHI 2020 · 被引用 29 次
- If beam search is the answer, what was the question?Clara Meister, Ryan Cotterell, Tim VieiraEMNLP 2020 · 被引用 26 次
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