Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification
Jai Gupta, Yi Tay, Chaitanya Kamath, Vinh Tran, Donald Metzler, Shailesh Bavadekar, Mimi Sun, Evgeniy Gabrilovich
Abstract
With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision.
BibTeX
@inproceedings{gupta-etal-2022-dense,
title = "Dense Feature Memory Augmented Transformers for {COVID}-19 Vaccination Search Classification",
author = "Gupta, Jai and
Tay, Yi and
Kamath, Chaitanya and
Tran, Vinh and
Metzler, Donald and
Bavadekar, Shailesh and
Sun, Mimi and
Gabrilovich, Evgeniy",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.53/",
doi = "10.18653/v1/2022.emnlp-industry.53",
pages = "521--530"
}