Enhancing Chinese Offensive Language Detection with Homophonic Perturbation (2025.emnlp-main)
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| Challenge: | Detecting offensive language in Chinese is challenging due to homophonic substitutions used to evade detection. |
| Approach: | They propose to use HED-COLD to build a large-scale homophonic dataset for Chinese offensive language detection and a homophone-aware pretraining strategy to learn phonetics and orthography. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the COLD test set and the toxicity benchmark ToxiCloakCN. |
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| Challenge: | Existing large language models struggle with systematically perturbed data designed to evade detection mechanisms. |
| Approach: | They propose a large language model with homophonic substitutions and emoji transformations to test their models' robustness against cloaking perturbations. |
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COLD: A Benchmark for Chinese Offensive Language Detection (2022.emnlp-main)
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| Challenge: | Offensive language detection is crucial for maintaining a civilized social media platform and deploying pre-trained language models. |
| Approach: | They propose a benchmark benchmark for Chinese offensive language analysis including a Chinese Offensive Language Dataset and a baseline detector which is trained on the dataset. |
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Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)
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Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen
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Shujian Yang, Shiyao Cui, Chuanrui Hu, Haicheng Wang, Tianwei Zhang, Minlie Huang, Jialiang Lu, Han Qiu
| Challenge: | Recent studies show that character substitutions in toxic Chinese text can confuse state-of-the-art LLMs. |
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Giving Control Back to Models: Enabling Offensive Language Detection Models to Autonomously Identify and Mitigate Biases (2024.findings-emnlp)
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| Challenge: | Existing models often rely on specific words to predict offensive content, compromising model fairness and potentially exacerbates biases against vulnerable and minority groups. |
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Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks (2024.findings-naacl)
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| Challenge: | Offensive language detection is important for filtering out abusive expressions, authors argue . authors propose user-intended adversarial attacks that insert special symbols or leverage distinctive features of the Korean language. |
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Towards Building a Robust Toxicity Predictor (2023.acl-industry)
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| Challenge: | Recent studies have focused on robustness of toxicity language predictors, but this is problematic for real-world toxicity detection. |
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HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models (2024.findings-emnlp)
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| Challenge: | Social media has amplified the propagation of hateful sentiments, highlighting the contested nature of "offensive content" research shows that "of offensive content" is still a contested construct due to varying definitions and labeling. |
| Approach: | They propose a dataset that features human-curated explanations for offensive content in English . they show that HateCOT pretraining improves performance of open-source LLMs . |
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Offensive Content Detection via Synthetic Code-Switched Text (2022.coling-1)
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| Challenge: | Existing methods to detect offensive content in social media platforms are limited by the availability of labeled code-switched data. |
| Approach: | They propose a method for generating synthetic code-switched offensive content data using human-generated data and a keyword classification baseline. |
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Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks (2022.findings-naacl)
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| Challenge: | Existing methods to improve model robustness against word substitution-based adversarial attacks are too slow to generate adversarials on the fly. |
| Approach: | They propose an approach to improve the robustness of BERT models against word substitution-based adversarial attacks by leveraging adversarials for self-supervised contrastive learning. |
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