DaN+: Danish Nested Named Entities and Lexical Normalization

TL;DR

This paper presents DaN+, a Danish corpus with nested named entities and lexical normalization, evaluating cross-lingual transfer, domain adaptation, and the impact of lexical normalization on NER performance.

Contribution

It introduces a new Danish multi-domain corpus and guidelines, and empirically compares transfer strategies, BERT models, and normalization effects for nested NER in a low-resource language.

Findings

Multi-task learning is the most robust NER strategy.

BERT models are sensitive to domain shifts.

Lexical normalization benefits low-canonical data.

Abstract

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.