Information systems, such as task management applications and digital assistants, can help people keep track of tasks of different types and different time durations, ranging from a few minutes to days or weeks. Helping people better manage their tasks and their time are core capabilities of assistive technologies, situated within a broader context of supporting more effective information access and use. Throughout the course of a day, there are typically many short time periods of downtime (e.g., five minutes or less) available to individuals. Microtasks are simple tasks that can be tackled in such short amounts of time. Identifying microtasks in task lists could help people utilize these periods of low activity to make progress on their task backlog. We define actionable tasks as self-contained tasks that need to be completed or acted on. However, not all to-do tasks are actionable. Many task lists are collections of miscellaneous items that can be completed at any time (e.g., books to read, movies to watch), notes (e.g., names, addresses), or the individual items are constituents in a list that is itself a task (e.g., a grocery list). In this article, we introduce the novel challenge of microtask detection, and we present machine-learned models for automatically determining which tasks are actionable and which of these actionable tasks are microtasks. Experiments show that our models can accurately identify actionable tasks, accurately detect actionable microtasks, and that we can combine these models to generate a solution that scales microtask detection to all tasks. We discuss our findings in detail, along with their limitations. These findings have implications for the design of systems to help people make the most of their time.

中文翻译：

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微任务检测

信息系统，例如任务管理应用程序和数字助理，可以帮助人们跟踪不同类型和不同持续时间的任务，从几分钟到几天或几周不等。帮助人们更好地管理他们的任务和他们的时间是辅助技术的核心能力，位于支持更有效的信息访问和使用的更广泛的背景下。在一天的整个过程中，通常存在许多可供个人使用的短时间段（例如，五分钟或更短时间）。微任务是可以在如此短的时间内完成的简单任务。识别任务列表中的微任务可以帮助人们利用这些低活动期来完成他们的任务积压。我们定义可操作的任务作为需要完成或采取行动的独立任务。但是，并非所有待办事项都是可操作的。许多任务列表是可以随时完成的杂项（例如，要阅读的书籍、要观看的电影）、注释（例如，姓名、地址）的集合，或者单个项目是列表中的组成部分，它本身就是一项任务（例如，杂货清单）。在本文中，我们介绍了微任务检测的新挑战，并提出了机器学习模型，用于自动确定哪些任务是可操作的，以及这些可操作任务中的哪些是微任务。实验表明，我们的模型可以准确地识别可操作的任务，准确地检测可操作的微任务，并且我们可以结合这些模型来生成将微任务检测扩展到所有任务的解决方案。我们详细讨论我们的发现，以及它们的局限性。这些发现对帮助人们充分利用时间的系统设计具有重要意义。