Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification
This work addresses the problem of data scarcity for researchers and practitioners in NLP, particularly in toxic speech detection to combat cyberbullying, but it is incremental as it applies existing methods to a specific domain.
The paper tackles data scarcity in machine learning by exploring data augmentation techniques like EDA and Backtranslation on the Wikipedia Toxic Comments dataset to build high-accuracy classifiers for toxic comment classification, finding that these techniques significantly boost classifier performance.
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.