What Matters When Building Universal Multilingual Named Entity Recognition Models?

תקציר מקורי באנגליתarXiv:2601.06347v2 Announce Type: replace Abstract: Recent progress in universal multilingual named entity recognition (NER) has been driven by multilingual transformer models, task-specific architectures, custom loss functions, and large-scale training datasets. However, despite substantial prior work, we find that many critical design decisions for such models are made without systematic justification, with individual components evaluated only in combination rather than in isolation. We argue that this lack of rigor impedes progress in the field by making it difficult to identify which choices improve multilingual generalization. In this work, we conduct extensive experiments on transformer backbones, architectures, training objectives, data composition, and threshold selection. Building