  Demographic inference from text has received a surge of attention in the
field of natural language processing in the last decade. In this paper, we use
personal names to infer religion in South Asia - where religion is a salient
social division, and yet, disaggregated data on it remains scarce. Existing
work predicts religion using dictionary based method, and therefore, can not
classify unseen names. We use character based models which learn character
patterns and, therefore, can classify unseen names as well with high accuracy.
These models are also much faster and can easily be scaled to large data sets.
We improve our classifier by combining the name of an individual with that of
their parent/spouse and achieve remarkably high accuracy. Finally, we trace the
classification decisions of a convolutional neural network model using
layer-wise relevance propagation which can explain the predictions of complex
non-linear classifiers and circumvent their purported black box nature. We show
how character patterns learned by the classifier are rooted in the linguistic
origins of names.
