A deep neural network based multi-task learning approach to hate speech detection

With the advent of the internet and numerous social media platforms, citizens now have enormous opportunities to express and share their opinions on various societal and political issues. This phenomenal growth of the internet, social media networks, and messaging platforms provide plenty of opportunities for building intelligent systems, but these are also being heavily misused by certain groups who often disseminate offensive, racial, and hate speeches. Hence, detecting hate speech at the right time plays a crucial role as its spread might affect social fabrics. In recent times, although a few benchmark datasets have emerged for hate speech detection, these are limited in volume and also do not follow any uniform annotation schema. In this paper, a deep multi-task learning (MTL) framework is proposed to leverage useful information from multiple related classification tasks in order to improve the performance of the individual task. The proposed multi-task model is based on the shared-private scheme that assigns shared and private layers to capture the shared-features and task-specific features from five classification tasks. Experiments1 on the 5 datasets show that the proposed framework attains encouraging performance in terms of macro-F1 and weighted-F1.