SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
This addresses the challenge of processing unstructured inked content for task management, though it is incremental as it builds on existing sequence labeling methods for a specific domain.
The paper tackles the problem of extracting tasks from free-form digitally handwritten notes by introducing SLATE, a sequence labeling approach that simultaneously performs sentence segmentation and classification, achieving a task F1 score of 84.4% and reducing latency by three times compared to a baseline.
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.