  We present SLATE, a sequence labeling approach for extracting tasks from
free-form content such as digitally handwritten (or "inked") notes on a virtual
whiteboard. Our approach allows us to create a single, low-latency model to
simultaneously perform sentence segmentation and classification of these
sentences into task/non-task sentences. SLATE greatly outperforms a baseline
two-model (sentence segmentation followed by classification model) approach,
achieving a task F1 score of 84.4%, a sentence segmentation (boundary
similarity) score of 88.4% and three times lower latency compared to the
baseline. Furthermore, we provide insights into tackling challenges of
performing NLP on the inking domain. We release both our code and dataset for
this novel task.
