Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection
Year: 2026
Type: conference-paper
Abstract: Flexible word boundaries and linguistic obfuscation, particularly slang, challenge precise span-level hate speech detection in Chinese.While benchmarks such as STATE Tox-iCN demand the exact extraction of Target-Argument-Hateful-Group quadruples, generative Large Language Models (LLMs) often fail strict boundary constraints.In contrast, discriminative 2D Grid Tagging methods frequently encounter label collisions.To resolve these problems, this study presents a Slangaware Label-Aligned Framework.A Structural-Semantic Lexicon Fusion (SSLF) module reduces ambiguity by mapping obscure slang to explicit hate semantics.Additionally, the proposed Label-Disentangled Volumetric Tagging (LDVT) projects token interactions into a volumetric space.LDVT uses task-specific branches and dedicated label channels to structurally mitigate feature interference.This approach removes label collisions without heuristic post-processing.Empirical outcomes on STATE ToxiCN indicate a Hard-F1 of 30.09%.This performance is 5.82% higher than the best fine-tuned LLM baseline and confirms the method is effective for exact-match extraction 1 .
Institutions: Xihua University, Tongji University, Chinese Academy of Sciences
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166885260