N\"urnberg NLP at ChildSafeAds 2026: Structurally Dissimilar Voter Ensembles under Four Levels of Data Access

Abstract:We describe the Nürnberg NLP system for ChildSafeAds 2026. The shared task asks what a monitoring system for commercial content in child-facing YouTube videos can achieve at a given level of data access. We answer with per-subtask ensembles of nine voters, organised into three branches that differ in backbone, adaptation method and class scope. Selection rests on channel-disjoint cross-validation, with the development set as a transfer check. The system wins two of the three subtasks. Its product-category score (ST2, 0.8243) and its compliance-flag score (ST3, 0.6530) are the best of the 22 final entries, and it places third on the task mean (0.7079). We further compare four access levels and report the cost at test-set scale.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.34986 [cs.CL]

(or arXiv:2609.34986v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.34986 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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