  Lack of sufficient labeled data often limits the applicability of advanced
machine learning algorithms to real life problems. However efficient use of
Transfer Learning (TL) has been shown to be very useful across domains. TL
utilizes valuable knowledge learned in one task (source task), where sufficient
data is available, to the task of interest (target task). In biomedical and
clinical domain, it is quite common that lack of sufficient training data do
not allow to fully exploit machine learning models. In this work, we present
two unified recurrent neural models leading to three transfer learning
frameworks for relation classification tasks. We systematically investigate
effectiveness of the proposed frameworks in transferring the knowledge under
multiple aspects related to source and target tasks, such as, similarity or
relatedness between source and target tasks, and size of training data for
source task. Our empirical results show that the proposed frameworks in general
improve the model performance, however these improvements do depend on aspects
related to source and target tasks. This dependence then finally determine the
choice of a particular TL framework.
