  We have released Sina-BERT, a language model pre-trained on BERT (Devlin et
al., 2018) to address the lack of a high-quality Persian language model in the
medical domain. SINA-BERT utilizes pre-training on a large-scale corpus of
medical contents including formal and informal texts collected from a variety
of online resources in order to improve the performance on health-care related
tasks. We employ SINA-BERT to complete following representative tasks:
categorization of medical questions, medical sentiment analysis, and medical
question retrieval. For each task, we have developed Persian annotated data
sets for training and evaluation and learnt a representation for the data of
each task especially complex and long medical questions. With the same
architecture being used across tasks, SINA-BERT outperforms BERT-based models
that were previously made available in the Persian language.
