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Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks
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The widespread dissemination of toxic online posts is increasingly damaging to society. However, research on detecting toxic language in Chinese has lagged significantly. Existing datasets lack fine-grained annotation of toxic types and expressions, and ignore the samples with indirect toxicity. In addition, it is crucial to introduce lexical knowledge to detect the toxicity of posts, which has been a challenge for researchers. In this paper, we facilitate the fine-grained detection of Chinese toxic language. First, we built Monitor Toxic Frame, a hierarchical taxonomy to analyze toxic types and expressions. Then, a fine-grained dataset ToxiCN is presented, including both direct and indirect toxic samples. We also build an insult lexicon containing implicit profanity and propose Toxic Knowledge Enhancement (TKE) as a benchmark, incorporating the lexical feature to detect toxic language. In the experimental stage, we demonstrate the effectiveness of TKE. After that, a systematic quantitative and qualitative analysis of the findings is given.
Forward citations
Cited by 4 Pith papers
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Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings
A new taxonomy and dataset of 8 types of perturbed toxic Chinese show nine top LLMs often miss these obfuscated insults, and small-sample ICL or fine-tuning causes overcorrection.
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Breaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon
C2TU combines a Chinese pronunciation graph, a toxic lexicon, and language-model probability checking to find and correct homophone-cloaked toxic words without any training.
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Chinese Toxic Language Mitigation via Sentiment Polarity Consistent Rewrites
A new Chinese detoxification dataset and 17-model benchmark show that LLMs can remove toxic words but often distort emotional tone, especially for emoji, homophone, and dialogue-based toxicity.
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FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion
FUSE models concepts as fuzzy set embeddings whose volume is a weighted sum over partitions and applies them to taxonomy expansion, reporting gains up to 23% over existing baselines.
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