  Deep neural networks have demonstrated their superior performance in almost
every Natural Language Processing task, however, their increasing complexity
raises concerns. In particular, these networks require high expenses on
computational hardware, and training budget is a concern for many. Even for a
trained network, the inference phase can be too demanding for
resource-constrained devices, thus limiting its applicability. The
state-of-the-art transformer models are a vivid example. Simplifying the
computations performed by a network is one way of relaxing the complexity
requirements. In this paper, we propose an end to end binarized neural network
architecture for the intent classification task. In order to fully utilize the
potential of end to end binarization, both input representations (vector
embeddings of tokens statistics) and the classifier are binarized. We
demonstrate the efficiency of such architecture on the intent classification of
short texts over three datasets and for text classification with a larger
dataset. The proposed architecture achieves comparable to the state-of-the-art
results on standard intent classification datasets while utilizing ~ 20-40%
lesser memory and training time. Furthermore, the individual components of the
architecture, such as binarized vector embeddings of documents or binarized
classifiers, can be used separately with not necessarily fully binary
architectures.
