This paper introduces DaN+, a new multi-domain corpus and annotation
guidelines for Danish nested named entities (NEs) and lexical normalization to
support research on cross-lingual cross-domain learning for a less-resourced
language. We empirically assess three strategies to model the two-layer Named
Entity Recognition (NER) task. We compare transfer capabilities from German
versus in-language annotation from scratch. We examine language-specific versus
multilingual BERT, and study the effect of lexical normalization on NER. Our
results show that 1) the most robust strategy is multi-task learning which is
rivaled by multi-label decoding, 2) BERT-based NER models are sensitive to
domain shifts, and 3) in-language BERT and lexical normalization are the most
beneficial on the least canonical data. Our results also show that an
out-of-domain setup remains challenging, while performance on news plateaus
quickly. This highlights the importance of cross-domain evaluation of
cross-lingual transfer.

介绍了 DaN+，一个新的、支持跨语种跨领域学习的丹麦嵌套命名实体（NEs）和词汇归一化的多领域语料库和标注指南。评估了三种策略来建模双层命名实体识别任务，证实多任务学习是最坚实的策略，并且在最规范的数据中，使用本语言 BERT 和词汇归一化是最有利的。