  In this paper, we propose an efficient transfer leaning methods for training
a personalized language model using a recurrent neural network with long
short-term memory architecture. With our proposed fast transfer learning
schemes, a general language model is updated to a personalized language model
with a small amount of user data and a limited computing resource. These
methods are especially useful for a mobile device environment while the data is
prevented from transferring out of the device for privacy purposes. Through
experiments on dialogue data in a drama, it is verified that our transfer
learning methods have successfully generated the personalized language model,
whose output is more similar to the personal language style in both qualitative
and quantitative aspects.
