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

Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs