A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Abstract

Cross-lingual biomedical named entity recognition (BioNER) remains challenging in low-resource settings due to scarce annotations, heterogeneous corpora, and severe label imbalance dominated by the O class. We aim to improve the robustness of BioNER fine-tuning under such conditions without modifying the backbone architecture. We propose RouteB-v4, a validation-driven training controller that adaptively reweights token-level loss using validation-side entity-level feedback. At periodic validation checkpoints, the controller performs conservative, bounded small-step updates to entity-type loss weights and explicitly regulates the dominant O-label weight to reduce training instability. The method is designed as a drop-in module for standard token-classification pipelines. We evaluate RouteB-v4 on CRAFT-based English-Spanish transfer settings, PharmaCoNER, and the external Spanish clinical benchmark CANTEMIST. Within the current experimental scope of English-Spanish transfer and Spanish biomedical/clinical datasets, RouteB-v4 achieves more stable span-level F1 improvements over strong static, heuristic dynamic, and learning-based non-RL dynamic reweighting baselines, with clearer gains on minority entity types and external generalization. Using XLM-R, RouteB-v4 reaches 0.868 F1 on PharmaCoNER and improves external generalization on CANTEMIST under the same training budget. Under a strict split-isolation protocol, the main performance advantage is largely retained after separating controller feedback from checkpoint selection. Within the current experimental scope of English-Spanish transfer and Spanish biomedical/clinical datasets, validation-driven adaptive loss control provides an effective way to improve the robustness of BioNER fine-tuning under label imbalance and distribution shift without altering the backbone architecture.