  While deep learning techniques have shown promising results in many natural
language processing (NLP) tasks, it has not been widely applied to the clinical
domain. The lack of large datasets and the pervasive use of domain-specific
language (i.e. abbreviations and acronyms) in the clinical domain causes slower
progress in NLP tasks than that of the general NLP tasks. To fill this gap, we
employ word/subword-level based models that adopt large-scale data-driven
methods such as pre-trained language models and transfer learning in analyzing
text for the clinical domain. Empirical results demonstrate the superiority of
the proposed methods by achieving 90.6% accuracy in medical domain natural
language inference task. Furthermore, we inspect the independent strengths of
the proposed approaches in quantitative and qualitative manners. This analysis
will help researchers to select necessary components in building models for the
medical domain.
