首页|Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation
Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation
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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.
State Key Lab of Intelligent Technology and Systems
Beijing National Research Center for Information Science and Technology
Tsinghua University,Beijing 100084,China
Graduate School of Information Science & Technology,The University of Tokyo,Tokyo 1138654,Japan
School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu,Sichuan,611731,China
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National Science Foundation for Distinguished Young ScholarsNSFC projectsNSFC projectsGuoqiang Institute of Tsinghua UniversityGuoqiang Institute of Tsinghua UniversityTsinghua-Toyota Joint Research Fund
NETL
NSTL
万方数据
