COLD: A Benchmark for Chinese Offensive Language Detection

• URL: http://arxiv.org/abs/2201.06025v1
• Date: Sun, 16 Jan 2022 11:47:23 GMT
• Title: COLD: A Benchmark for Chinese Offensive Language Detection
• Authors: Jiawen Deng, Jingyan Zhou, Hao Sun, Fei Mi, Minlie Huang
• Abstract summary: We use COLDataset, a Chinese offensive language dataset with 37k annotated sentences. We also propose textscCOLDetector to study output offensiveness of popular Chinese language models. Our resources and analyses are intended to help detoxify the Chinese online communities and evaluate the safety performance of generative language models.
• Score: 54.60909500459201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offensive language detection and prevention becomes increasing critical for maintaining a healthy social platform and the safe deployment of language models. Despite plentiful researches on toxic and offensive language problem in NLP, existing studies mainly focus on English, while few researches involve Chinese due to the limitation of resources. To facilitate Chinese offensive language detection and model evaluation, we collect COLDataset, a Chinese offensive language dataset containing 37k annotated sentences. With this high-quality dataset, we provide a strong baseline classifier, COLDetector, with 81% accuracy for offensive language detection. Furthermore, we also utilize the proposed \textsc{COLDetector} to study output offensiveness of popular Chinese language models (CDialGPT and CPM). We find that (1) CPM tends to generate more offensive output than CDialGPT, and (2) certain type of prompts, like anti-bias sentences, can trigger offensive outputs more easily.Altogether, our resources and analyses are intended to help detoxify the Chinese online communities and evaluate the safety performance of generative language models. Disclaimer: The paper contains example data that may be considered profane, vulgar, or offensive.

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