MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 5393–5403 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 5393 MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed Sujay Kumar Jauhar, Nirupama Chandrasekaran, Michael Gamon, Ryen W. White Microsoft Research Redmond, WA, USA {sjauhar, niruc, mgamon, ryenw}@microsoft.com Abstract Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage, and act on them. These digital tools – such as task management applications – provide a unique opportunity to study and understand tasks and their connection to the real world, and through intelligent assistance, help people be more productive. By logging signals such as text, timestamp information, and social connectivity graphs, an increasingly rich and detailed picture of how tasks are created and organized, what makes them important, and who acts on them, can be progressively developed. Yet the context around actual task completion remains fuzzy, due to the basic disconnect between actions taken in the real world and telemetry recorded in the digital world. Thus, in this paper we compile and release a novel, real-life, large-scale dataset called MS-LaTTE that captures two core aspects of the context surrounding task completion: location and time. We describe our annotation framework and conduct a number of analyses on the data that were collected, demonstrating that it captures intuitive contextual properties for common tasks. Finally, we test the dataset on the two problems of predicting spatial and temporal task co-occurrence, concluding that predictors for co-location and co-time are both learnable, with a BERT fine-tuned model outperforming several other baselines. The MS-LaTTE dataset provides an opportunity to tackle many new modeling challenges in contextual task understanding and we hope that its release will spur future research in task intelligence more broadly. Keywords: Task completion, context modeling, location, time 1. Introduction Tasks are the primary unit of personal and professional productivity. People capture, organize, track and com- plete tasks as a way to measure and make progress to- wards their goals. Task management strategies range from scribbled sticky notes on refrigerators to com- plex group collaboration platforms such as Microsoft Planner, and everything in between. Digital tools for task management support are prevalent in a range of applications including electronic mail (Bellotti et al., 2003), to-do applications (Bellotti et al., 2004) and dig- ital assistants (Graus et al., 2016), and for a range of user scenarios such as contextual reminders (Kamar and Horvitz, 2011), task duration estimation (White and Hassan Awadallah, 2019), and complex task de- composition (Zhang et al., 2021b). In short, digital task management provides an important opportunity to make users more productive, and help them balance their priorities in a connected world where the bound- aries between life and work are often blurred. Many applications record and use several signals such as the text of a task, the time it was created, its impor- tance, due-date, and who …

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