Evgeniy Gabrilovich
2022
Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification
Jai Gupta
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Yi Tay
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Chaitanya Kamath
|
Vinh Tran
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Donald Metzler
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Shailesh Bavadekar
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Mimi Sun
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Evgeniy Gabrilovich
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
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.
2008
Introduction to Computational Advertising
Evgeniy Gabrilovich
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Vanja Josifovski
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Bo Pang
Tutorial Abstracts of ACL-08: HLT
1998
System Demonstration Natural Language Generation With Abstract Machine
Evgeniy Gabrilovich
|
Nissirn Francez
|
Shuly Wintner
Natural Language Generation
Search
Co-authors
- Nissirn Francez 1
- Shuly Wintner 1
- Jai Gupta 1
- Yi Tay 1
- Chaitanya Kamath 1
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