Improving the Detection of Multilingual South African Abusive Language via Skip-gram using Joint Multilevel Domain Adaptation

Abstract

The distinctiveness and sparsity of low-resource multilingual South African abusive language necessitate the development of a novel solution to automatically detect different classes of abusive language instances using machine learning. Skip-gram has been used to address sparsity in machine learning classification problems but is inadequate in detecting South African abusive language due to the considerable amount of rare features and class imbalance. Joint Domain Adaptation has been used to enlarge features of a low-resource target domain for improved classification outcomes by jointly learning from the target domain and large-resource source domain. This paper, therefore, builds a Skip-gram model based on Joint Domain Adaptation to improve the detection of multilingual South African abusive language. Contrary to the existing Joint Domain Adaptation approaches, a Joint Multilevel Domain Adaptation model involving adaptation of monolingual source domain instances and multilingual target domain instances with high frequency of rare features was executed at the first level, and adaptation of target-domain features and first-level features at the next level. Both surface-level and embedding word features were used to evaluate the proposed model. In the evaluation of surface-level features, the Joint Multilevel Domain Adaptation model outperformed the state-of-the-art models with accuracy of 0.92 and F1-score of 0.68. In the evaluation of embedding features, the proposed model outperformed the state-of-the-art models with accuracy of 0.88 and F1-score of 0.64. The Joint Multilevel Domain Adaptation model significantly improved the average information gain of the rare features in different language categories and reduced class imbalance.