The vast majority of the popular English named entity recognition (NER)
datasets contain American or British English data, despite the existence of
many global varieties of English. As such, it is unclear whether they
generalize for analyzing use of English globally. To test this, we build a
newswire dataset, the Worldwide English NER Dataset, to analyze NER model
performance on low-resource English variants from around the world. We test
widely used NER toolkits and transformer models, including models using the
pre-trained contextual models RoBERTa and ELECTRA, on three datasets: a
commonly used British English newswire dataset, CoNLL 2003, a more American
focused dataset OntoNotes, and our global dataset. All models trained on the
CoNLL or OntoNotes datasets experienced significant performance drops-over 10
F1 in some cases-when tested on the Worldwide English dataset. Upon examination
of region-specific errors, we observe the greatest performance drops for
Oceania and Africa, while Asia and the Middle East had comparatively strong
performance. Lastly, we find that a combined model trained on the Worldwide
dataset and either CoNLL or OntoNotes lost only 1-2 F1 on both test sets.

研究了流行的英语命名实体识别（NER）数据集是否能够概括全球范围内的英语使用，通过建立全球英语 NER 数据集并测试多个模型，发现它们在处理低资源的全球英语变体时都出现了显著的性能下降。