Proposes JFPD with uncertainty and semantic trust weighting for reliable domain adaptation under distribution shift.
Do language models understand time?
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.
citing papers explorer
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Trust-Aware Joint Feature-Prediction Discrepancy for Robust Domain Adaptation
Proposes JFPD with uncertainty and semantic trust weighting for reliable domain adaptation under distribution shift.
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A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts
Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.