DaN+: Danish Nested Named Entities and Lexical Normalization
Barbara Plank, Kristian Nørgaard Jensen, Rob van der Goot · 2020
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.