  In practice, it is common to find oneself with far too little text data to
train a deep neural network. This "Big Data Wall" represents a challenge for
minority language communities on the Internet, organizations, laboratories and
companies that compete the GAFAM (Google, Amazon, Facebook, Apple, Microsoft).
While most of the research effort in text data augmentation aims on the
long-term goal of finding end-to-end learning solutions, which is equivalent to
"using neural networks to feed neural networks", this engineering work focuses
on the use of practical, robust, scalable and easy-to-implement data
augmentation pre-processing techniques similar to those that are successful in
computer vision. Several text augmentation techniques have been experimented.
Some existing ones have been tested for comparison purposes such as noise
injection or the use of regular expressions. Others are modified or improved
techniques like lexical replacement. Finally more innovative ones, such as the
generation of paraphrases using back-translation or by the transformation of
syntactic trees, are based on robust, scalable, and easy-to-use NLP Cloud APIs.
All the text augmentation techniques studied, with an amplification factor of
only 5, increased the accuracy of the results in a range of 4.3% to 21.6%, with
significant statistical fluctuations, on a standardized task of text polarity
prediction. Some standard deep neural network architectures were tested: the
multilayer perceptron (MLP), the long short-term memory recurrent network
(LSTM) and the bidirectional LSTM (biLSTM). Classical XGBoost algorithm has
been tested with up to 2.5% improvements.
