构建通用多语言命名实体识别模型时什么更重要？
What Matters When Building Universal Multilingual Named Entity Recognition Models?
AI总结：
本文提出Otter模型，通过系统性实验改进多语言NER，实现超过100种语言的高效高精度识别，F1得分超越GLiNER-x-base 5.3个百分点。
摘要：
最近通用多语言命名实体识别（NER）的进步受到多语言Transformer模型和任务特定架构、损失函数和训练数据集的进展推动。尽管已有大量先前工作，但我们发现许多关键设计决策在没有系统性论证的情况下被做出，架构组件、训练目标和数据来源仅在组合中被评估，而不是单独评估。我们主张这些决定阻碍了该领域的发展，因为它们使难以确定哪些选择能提高模型性能。在本工作中，我们围绕架构、Transformer骨干网络、训练目标和数据组成，在广泛的语言范围内进行了大量实验。基于这些见解，我们引入了Otter，一个支持超过100种语言的通用多语言NER模型。Otter在强大的多语言NER基线上实现了持续的改进，其在F1得分上比GLiNER-x-base高出5.3个百分点，并与大型生成模型如Qwen3-32B具有竞争性的性能，同时效率显著更高。我们发布了模型检查点、训练和评估代码，以促进可重复性和未来研究。
英文摘要：
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 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 impedes progress in the field by making it difficult to identify which choices improve multilingual generalization. In this work, we conduct an extensive empirical evaluation on transformer backbones, architectures, training objectives, data composition, and threshold selection for zero-shot, multilingual NER models. Building on these findings, we present Otter, a small encoder model that achieves consistent improvements over strong multilingual NER baselines, outperforming similarly sized models by 5.7 percentage points in F1. Further, it remains within 1.4 points of Qwen3-32B and 5.4 points below Gemma3-27B, while being roughly 90x smaller and one to two orders of magnitude faster at inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
