  This paper presents our approach for task 2 and task 3 of Social Media Mining
for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate
adverse drug reaction (ADR) tweets from nonADR tweets and is treated as binary
classification. Task3 involves extracting ADR mentions and then mapping them to
MedDRA codes. Extracting ADR mentions is treated as sequence labeling and
normalizing ADR mentions is treated as multi-class classification. Our system
is based on pre-trained language model RoBERTa and it achieves a) F1-score of
58% in task2 which is 12% more than the average score b) relaxed F1-score of
70.1% in ADR extraction of task 3 which is 13.7% more than the average score
and relaxed F1-score of 35% in ADR extraction + normalization of task3 which is
5.8% more than the average score. Overall, our models achieve promising results
in both the tasks with significant improvements over average scores.
