Noise-Robust Financial Numerical Entity Attribute Tagging

The paper introduces NORA, a noise‑robust tagging system for Financial Numerical Entity (FNE) attributes, addressing errors in XBRL‑derived labels and under‑emphasized attributes such as reporting‑time relation, measurement scale, and accounting sign. NORA employs task‑aware instance‑specific weighting to reduce the impact of noisy labels during training and proposes an evaluation protocol that filters noisy labels in real‑world test sets. A large benchmark of 11.4 million FNEs across 6.3 million sentences is constructed, and experiments show NORA outperforms prior models in mean Accuracy and Macro F1, especially for time‑relation prediction.

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