Problem
Research questions and friction points this paper is trying to address.
hate speech parsing
cultural challenges
linguistic challenges
social-group challenges
SPAR-Hate: An Auditor-Guided Multi-Agent Framework for Bilingual Hate Speech Parsing
🤖 AI Summary
为解决文化、语言和社会群体挑战，提出SPAR-Hate框架，通过多代理和证据约束仲裁过程改进双语仇恨言论解析。
📝 Abstract
Hate speech detection has recently shifted from coarse-grained classification to structured parsing, where systems must jointly identify hateful targets, arguments, and target-level labels. However, existing studies primarily emphasize benchmark evaluation while paying less attention to the cultural, linguistic, and social-group challenges involved in structured hate speech parsing. To address these challenges, we propose SPAR-Hate, an auditor-guided multi-agent framework for bilingual hate speech parsing. The framework first decomposes documents into clause-level decision units and then generates evidence-grounded judgments from three complementary perspectives: Victim, Moderator, and Cultural Bystander. An evidence-constrained arbitration process resolves conflicts among role-specific predictions and aggregates them into structured sample-level outputs. Experiments on the STATE-ToxiCN and TBO benchmarks show that SPAR-Hate consistently improves bilingual hate parsing across diverse large language models. The framework achieves state-of-the-art results on bilingual multi-tuple extraction tasks, with the largest gains observed under stricter structural evaluation metrics.
Innovation
Methods, ideas, or system contributions that make the work stand out.
auditor-guided multi-agent framework
bilingual hate speech parsing
evidence-grounded judgments
evidence-constrained arbitration process
structured sample-level outputs
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