Hate speech research has moved from coarse-grained classification towards structured parsing, where systems jointly identify targets, supporting arguments, and target-level labels. Documents with multiple targets, conflicting local readings, or culturally coded language make these bindings difficult to recover. SPAR-Hate is an auditor-guided multi-perspective role-reasoning framework for bilingual hate speech parsing. It decomposes each document into local focus units, elicits evidence-grounded candidates from Victim, Moderator, and Cultural Bystander perspectives, resolves candidate conflicts under grounding and schema constraints, and reassembles sample-level predictions. Experiments on STATE-ToxiCN and a controlled TBO split show gains across local and API backbones, concentrated on strict joint target--argument--label metrics. Controlled comparisons favour separate candidate generation and arbitration for joint parsing. Within the tested pipeline, role-conditioned prompting yields higher exact-match joint scores than repeated generic prompting, while Chinese overlap-based results depend on the backbone. Structured teacher traces also support training a smaller student model.

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