Label Unification for Cross-Dataset Generalization in Cybersecurity NER

Abstract:The field of cybersecurity NER lacks standard-ized labels, making it challenging to combine datasets. We investigate label unification across four cybersecurity datasets to increase data resource usability. We perform a coarse-grained label unification and conduct pairwise cross-dataset evaluations using RoBERTa models. Qualitative analysis of predictions reveals errors, limitations, and dataset differences. To address unification limitations, we propose alternative architectures including a multi-head model and a graph-based transfer model. Under our selected mapping and training setup, unified training underperformed the corresponding single-dataset baselines on all four development sets. The multi-head model with weight sharing provides no consistent benefits over single-dataset baselines, and the graph-based transfer model shows no consistent benefits.

Computation and Language (cs.CL)

arXiv:2507.13870 [cs.CL]

(or arXiv:2507.13870v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2507.13870 Focus to learn more arXiv-issued DOI via DataCite

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