  We present an algorithm based on multi-layer transformers for identifying
Adverse Drug Reactions (ADR) in social media data. Our model relies on the
properties of the problem and the characteristics of contextual word embeddings
to extract two views from documents. Then a classifier is trained on each view
to label a set of unlabeled documents to be used as an initializer for a new
classifier in the other view. Finally, the initialized classifier in each view
is further trained using the initial training examples. We evaluated our model
in the largest publicly available ADR dataset. The experiments testify that our
model significantly outperforms the transformer-based models pretrained on
domain-specific data.
