Classifying human rights violations using deep multi-label co-training

This paper presents a multi-labeled semi-supervised study that includes a small labeled dataset and an unlabeled dataset. With the co-training framework, we combine a two-view semi-supervised learning for text classification by implementing two neural networks. As in the original co-training paradigm, the text is classified according to two independent learner views using two separate classifiers. In order to extend this idea to deep learning, the deep co-training model uses deep neural networks to train on different views of generated samples to calculate similarity in the probability distribution of predicted outcomes. This co-training framework depends on co-trained networks in order to classify text into multiple labels. Furthermore, the method adds noise to keep the classifier from being affected by it during prediction. As a result, such co-trained networks provide more relevant data and improve classification accuracy. To demonstrate the effectiveness of the developed approach, we compare it to state-of-the-art machine learning and deep learning classifiers on a dataset of survivor stories of human rights violations.