  Transfer Learning (TL) plays a crucial role when a given dataset has
insufficient labeled examples to train an accurate model. In such scenarios,
the knowledge accumulated within a model pre-trained on a source dataset can be
transferred to a target dataset, resulting in the improvement of the target
model. Though TL is found to be successful in the realm of image-based
applications, its impact and practical use in Natural Language Processing (NLP)
applications is still a subject of research. Due to their hierarchical
architecture, Deep Neural Networks (DNN) provide flexibility and customization
in adjusting their parameters and depth of layers, thereby forming an apt area
for exploiting the use of TL. In this paper, we report the results and
conclusions obtained from extensive empirical experiments using a Convolutional
Neural Network (CNN) and try to uncover thumb rules to ensure a successful
positive transfer. In addition, we also highlight the flawed means that could
lead to a negative transfer. We explore the transferability of various layers
and describe the effect of varying hyper-parameters on the transfer
performance. Also, we present a comparison of accuracy value and model size
against state-of-the-art methods. Finally, we derive inferences from the
empirical results and provide best practices to achieve a successful positive
transfer.
