SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
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.
Cite
@article{arxiv.2211.04454,
title = {SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content},
author = {Apurva Gandhi and Ryan Serrao and Biyi Fang and Gilbert Antonius and Jenna Hong and Tra My Nguyen and Sheng Yi and Ehi Nosakhare and Irene Shaffer and Soundararajan Srinivasan and Vivek Gupta},
journal= {arXiv preprint arXiv:2211.04454},
year = {2022}
}
Comments
Accepted at EMNLP 2022 as an Industry Track paper