Jensen, Kristian Nørgaard

Plank, Barbara

van der Goot, Rob

English

This paper introduces DAN+, a new multi-domain corpus and annotation guidelines for Dan- ish 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.<br/

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DAN+: Danish Nested Named Entities and Lexical Normalization

https://pure.itu.dk/portal/files/85472827/COLING2020_Dan_.pdf

DAN+: Danish Nested Named Entities and Lexical Normalization

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