  In this paper, we present our work participating in the BioCreative VII Track
3 - automatic extraction of medication names in tweets, where we implemented a
multi-task learning model that is jointly trained on text classification and
sequence labelling. Our best system run achieved a strict F1 of 80.4, ranking
first and more than 10 points higher than the average score of all
participants. Our analyses show that the ensemble technique, multi-task
learning, and data augmentation are all beneficial for medication detection in
tweets.
