SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content

TL;DR

SLATE is a novel sequence labeling model that efficiently extracts and classifies tasks from free-form handwritten notes, outperforming traditional methods in accuracy and speed.

Contribution

The paper introduces SLATE, a unified low-latency sequence labeling approach for task extraction from inked content, with improved performance over baseline methods.

Findings

Achieves 84.4% task F1 score

88.4% sentence boundary accuracy

Three times lower latency than baseline

Abstract

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.