  This paper tackles one of the greatest limitations in Machine Learning: Data
Scarcity. Specifically, we explore whether high accuracy classifiers can be
built from small datasets, utilizing a combination of data augmentation
techniques and machine learning algorithms. In this paper, we experiment with
Easy Data Augmentation (EDA) and Backtranslation, as well as with three popular
learning algorithms, Logistic Regression, Support Vector Machine (SVM), and
Bidirectional Long Short-Term Memory Network (Bi-LSTM). For our
experimentation, we utilize the Wikipedia Toxic Comments dataset so that in the
process of exploring the benefits of data augmentation, we can develop a model
to detect and classify toxic speech in comments to help fight back against
cyberbullying and online harassment. Ultimately, we found that data
augmentation techniques can be used to significantly boost the performance of
classifiers and are an excellent strategy to combat lack of data in NLP
problems.
