论文标题

基于Web的虚拟助手技能的多模式最终用户编程

Multi-Modal End-User Programming of Web-Based Virtual Assistant Skills

论文作者

Fischer, Michael H., Campagna, Giovanni, Choi, Euirim, Lam, Monica S.

论文摘要

尽管Alexa可以在纸上执行超过100,000个技能,但其功能仅涵盖了网络上可能的一小部分。为了充分发挥助手的潜力,希望个人可以创建技能来自动化其个人网络浏览程序。但是,许多看似简单的例程,例如监视与家乡的Covid-19统计数据,在线检测孩子成绩的变化,或者在没有常规的编程概念(例如有条件和迭代评估)的情况下,无法自动化自己的成绩。本文介绍了Vash(语音助手脚本辅助人员),这是一个新系统,促进用户创建有用的基于网络的虚拟助手技能而无需学习正式的编程语言。使用Vash,用户展示了他们在浏览器中的关注任务,并发出了一些语音命令,例如命名技能并增加了操作的条件。 Vash将这些多模式规格转变为可以在虚拟助手上调用发票的技能。这些技能用我们设计的正式编程语言表示,该语言称为WebTalk,该语言支持参数化,功能调用,条件和迭代执行。 Vash是一个完全有效的原型,可在现实世界网站上的Chrome浏览器上使用。我们的用户研究表明,用户有许多他们希望自动化的Web例程,其中81%可以使用VASH表示。我们发现Vash很容易学习,并且我们研究中的大多数用户都希望使用我们的系统。

While Alexa can perform over 100,000 skills on paper, its capability covers only a fraction of what is possible on the web. To reach the full potential of an assistant, it is desirable that individuals can create skills to automate their personal web browsing routines. Many seemingly simple routines, however, such as monitoring COVID-19 stats for their hometown, detecting changes in their child's grades online, or sending personally-addressed messages to a group, cannot be automated without conventional programming concepts such as conditional and iterative evaluation. This paper presents VASH (Voice Assistant Scripting Helper), a new system that empowers users to create useful web-based virtual assistant skills without learning a formal programming language. With VASH, the user demonstrates their task of interest in the browser and issues a few voice commands, such as naming the skills and adding conditions on the action. VASH turns these multi-modal specifications into skills that can be invoked invoice on a virtual assistant. These skills are represented in a formal programming language we designed called WebTalk, which supports parameterization, function invocation, conditionals, and iterative execution. VASH is a fully working prototype that works on the Chrome browser on real-world websites. Our user study shows that users have many web routines they wish to automate, 81% of which can be expressed using VASH. We found that VASH Is easy to learn, and that a majority of the users in our study want to use our system.

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