  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.
