CommandSpace:对任务、描述和特性之间的关系进行建模

Eytan Adar, Mira Dontcheva, Gierad Laput
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引用次数: 38

摘要

用户经常用与应用程序领域语言非常不同的语言来描述他们想要用应用程序完成的任务。为了解决系统语言和人类语言之间的差距,我们建议使用深度学习技术挖掘关于应用程序的大量Web文档语料库,从而对应用程序的领域语言进行建模。高维向量空间表示可以对用户任务、系统命令和自然语言描述之间的关系进行建模,并支持映射操作,例如在给定自然语言查询的情况下识别可能的系统命令,以及在给定用户操作跟踪的情况下识别用户任务。我们用一个系统CommandSpace演示了这种方法的可行性,该系统用于流行的照片编辑应用程序Adobe Photoshop。我们构建并评估了模型支持的几个应用程序,展示了这种方法的强大功能和灵活性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CommandSpace: modeling the relationships between tasks, descriptions and features
Users often describe what they want to accomplish with an application in a language that is very different from the application's domain language. To address this gap between system and human language, we propose modeling an application's domain language by mining a large corpus of Web documents about the application using deep learning techniques. A high dimensional vector space representation can model the relationships between user tasks, system commands, and natural language descriptions and supports mapping operations, such as identifying likely system commands given natural language queries and identifying user tasks given a trace of user operations. We demonstrate the feasibility of this approach with a system, CommandSpace, for the popular photo editing application Adobe Photoshop. We build and evaluate several applications enabled by our model showing the power and flexibility of this approach.
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