Identifying Hate Speech Through Syntax Dependency Graph Convolution and Sentiment Knowledge Transfer

Abstract

In recent years, hate speech spread on the Internet has seriously affected society’s harmony, stability, and development. A way to quickly identify hate speech from the vast amount of data on the Internet is urgent. In this paper, different from previous traditional methods, we explore a novel scenario of constructing a syntax dependency graph for each instance based on the syntactical information retrieved from an external tool. We propose a model called the Dependency Graph Convolutional and Sentiment Knowledge Transfer (DGCSKT) network. DGCSKT utilizes syntactic dependency graphs and dependency graph convolutional operations to enhance the model’s ability to perceive contextual information. Additionally, we introduce sentiment resources that are data-homogeneous as an auxiliary task at the bottom level of the model to share effective sentiment features and improve recognition performance. Then, we propose the Dynamic Normalized Weighting (DNW) method to weight the training information of different tasks and thus improve the model’s generalization ability. Compared to the current state-of-the-art methods, our proposed approach improves the Macro-F1 by 3.88% and 0.54% in OLID and HateEval respectively.

Keywords

• Hate speech detection
• multi-task optimization
• dependency graph convolution
• dynamic normalized weighting