A Toxic Comment Classification Model Based on Ensemble

Abstract

Accurate classification of toxic comments in different languages is an important task in today's international social networking platforms. In order to improve the classification of toxic comments in different languages, this paper combines the advantages of high accuracy of monolingual model and strong generalization ability of multi-language model, and adopts the ensemble of multilingual model and monolingual model to classify toxic comments. For the monolingual model, the monolingual pre-training model is fine-tuned with labeled task data; for the multilingual model, before fine-tuning the model with labeled task data, a further pre-training is applied to train model on unlabeled data, which aims to make full use of the large amount of unlabeled data and reduce the dependency of amount of labeled comment data while improving the classification effect. Comparative experiments on Conversasion AI's multilingual toxic comment dataset show that the model in this paper has improved results on different evaluation metrics compared to the XLM-RoBERTa multilingual fine-tuning model, illustrating the validity of the model.