arXiv:2605.24910v2 Announce Type: replace
Abstract: Financial Numerical Entity (FNE) understanding aims to recover the meaning of numerical mentions in financial reports. Existing studies focus on concept name prediction and face two important limitations. First, labels derived from inline XBRL may contain errors because filings are usually prepared manually. Second, other important FNE attributes, such as reporting-time relation, measurement scale, and accounting sign, are less emphasized. We propose NOise-Robust Tagging for Rich Financial Numerical Entity Attributes (NORA) to address these gaps. NORA uses task-aware instance-specific weighting to attenuate the influence of noisy labels during training, and we develop an evaluation protocol that filters noisy labels in real-world test sets. In addition, we construct a large-scale benchmark containing 11.4 million FNEs in 6.3 million sentences with multi-attribute labels and filing metadata. Experiments show that NORA performs strongly compared with previous financial numeric tagging models and noisy-label baselines. Under both unfiltered and noise-filtered test settings, it achieves the best mean Accuracy and Macro F1 over the four attributes, with the largest gain on time-relation prediction, while remaining competitive on concept name, scale, and sign. These results demonstrate the value of modeling rich FNE attributes with document context while accounting for label noise in real-world financial filings.
Abstract: Financial Numerical Entity (FNE) understanding aims to recover the meaning of numerical mentions in financial reports. Existing studies focus on concept name prediction and face two important limitations. First, labels derived from inline XBRL may contain errors because filings are usually prepared manually. Second, other important FNE attributes, such as reporting-time relation, measurement scale, and accounting sign, are less emphasized. We propose NOise-Robust Tagging for Rich Financial Numerical Entity Attributes (NORA) to address these gaps. NORA uses task-aware instance-specific weighting to attenuate the influence of noisy labels during training, and we develop an evaluation protocol that filters noisy labels in real-world test sets. In addition, we construct a large-scale benchmark containing 11.4 million FNEs in 6.3 million sentences with multi-attribute labels and filing metadata. Experiments show that NORA performs strongly compared with previous financial numeric tagging models and noisy-label baselines. Under both unfiltered and noise-filtered test settings, it achieves the best mean Accuracy and Macro F1 over the four attributes, with the largest gain on time-relation prediction, while remaining competitive on concept name, scale, and sign. These results demonstrate the value of modeling rich FNE attributes with document context while accounting for label noise in real-world financial filings.
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