关键词: 反讽检测, 攻击性言论检测, 细粒度特征, 隐晦攻击, 注意力机制

Abstract: Offensive speech spread on the Internet seriously disrupts the normal network order and destroys the network environment for healthy communication. Existing detection technologies are more focused on the distinctive features in the text, and it is difficult to discover more subtle attack methods. In response to the above problems, an offensive speech detection model BSWD (BERT-based Sarcasm and Word Detection) incorporating irony mechanism is proposed. First, a model Sarcasm-BERT based on the irony mechanism is proposed to detect semantic conflicts in speech; secondly, a fine-grained vocabulary offensive feature extraction model WordsDetect is proposed to detect offensive words in speech; finally, the BSWD model is obtained by fusing the two models to improve the detection accuracy.The experimental results show that the accuracy, precision, recall, and F1 score indicators of the proposed model are generally improved by more than 2%, compared with the BERT(Bidirectional Encoder Representation from Transformers) and HateBERT methods, which significantly improves the detection performance and can better detect implicit offensive speech. Compared with the SKS (Sentiment Knowledge Sharing) and BiCHAT (Bidirectional Long Short-Term Memory with deep Convolution neural network and Hierarchical ATtention) methods, it has stronger generalization ability and robustness. The results of the ablation experiment show that the proposed model has an improvement of 0.5%~1.5%, compared with WordsDetect and Sarcasm-BERT in terms of precision, recall, and F1 score indicators, successfully integrating the advantages of each part of the work.

Key words: irony detection, offensive speech detection, fine-grained feature, obscure attack, attention mechanism