Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. Our model outperforms the prior work by 9.53% F1-Score.
@article{arxiv.2011.00057,
title = {A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification},
author = {Harshit Jain and Nishant Raj and Suyash Mishra},
journal= {arXiv preprint arXiv:2011.00057},
year = {2021}
}