Exploration of Multi-corpus Learning for Hate Speech Classification in Low Resource Scenarios

Abstract

AbstractThe dramatic increase in social media has given rise to the problem of online hate speech. Deep neural network-based classifiers have become the state-of-the-art for automatic hate speech classification. The performance of these classifiers depends on the amount of available labelled training data. However, most hate speech corpora have a small number of hate speech samples. In this article, we aim to jointly use multiple hate speech corpora to improve hate speech classification performance in low-resource scenarios. We harness different hate speech corpora in a multi-task learning setup by associating one task to one corpus. This multi-corpus learning scheme is expected to improve the generalization, the latent representations, and domain adaptation of the model. Our work evaluates multi-corpus learning for hate speech classification and domain adaptation. We show significant improvements in classification and domain adaptation in low-resource scenarios. Keywordshate speech detectionmulti-task learninglow-resource text classification