Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation.

Abstract

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms, as well as promoting the safe deployment of large language models. Training data is the basis for developing detectors; however, the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English resources. This significantly affects the accuracy of Chinese offensive language detectors in practical applications, especially when dealing with hard cases or out-of-domain samples. To alleviate the limitations posed by available datasets, we introduce AugCOLD (Augmented Chinese Offensive Language Dataset), a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model generation. Furthermore, we employ a multiteacher distillation framework to enhance detection performance with unsupervised data. That is, we build multiple teachers with publicly accessible datasets and use them to assign soft labels to AugCOLD. The soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network, i.e., the final offensive detector. We conduct experiments on multiple public test sets and our well-designed hard tests, demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.