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.
Anthology ID:
2022.emnlp-industry.53
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Editors:
Yunyao Li, Angeliki Lazaridou
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
521–530
Language:
URL:
https://aclanthology.org/2022.emnlp-industry.53
DOI:
10.18653/v1/2022.emnlp-industry.53
Bibkey:
Cite (ACL):
Jai Gupta, Yi Tay, Chaitanya Kamath, Vinh Tran, Donald Metzler, Shailesh Bavadekar, Mimi Sun, and Evgeniy Gabrilovich. 2022. Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 521–530, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification (Gupta et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-industry.53.pdf