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This paper tackles one of the greatest limitations in Machine Learning: DataScarcity. Specifically, we explore whether high accuracy classifiers can bebuilt from small datasets, utilizing a combination of data augmentationtechniques and machine learning algorithms. In this paper, we experiment withEasy Data Augmentation (EDA) and Backtranslation, as well as with three popularlearning algorithms, Logistic Regression, Support Vector Machine (SVM), andBidirectional Long Short-Term Memory Network (Bi-LSTM). For ourexperimentation, we utilize the Wikipedia Toxic Comments dataset so that in theprocess of exploring the benefits of data augmentation, we can develop a modelto detect and classify toxic speech in comments to help fight back againstcyberbullying and online harassment. Ultimately, we found that dataaugmentation techniques can be used to significantly boost the performance ofclassifiers and are an excellent strategy to combat lack of data in NLPproblems.

Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification