Abstract
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-lingualcross-domain learning for a less-resourced language. We empirically assess three strategies tomodel the two-layer Named Entity Recognition (NER) task. We compare transfer capabilitiesfrom German versus in-language annotation from scratch. We examine language-specific versusmultilingual 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 lexicalnormalization are the most beneficial on the least canonical data. Our results also show that anout-of-domain setup remains challenging, while performance on news plateaus quickly. Thishighlights the importance of cross-domain evaluation of cross-lingual transfer.- Anthology ID:
- 2020.coling-main.583
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6649–6662
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.583
- DOI:
- 10.18653/v1/2020.coling-main.583
- Cite (ACL):
- Barbara Plank, Kristian Nørgaard Jensen, and Rob van der Goot. 2020. DaN+: Danish Nested Named Entities and Lexical Normalization. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6649–6662, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- DaN+: Danish Nested Named Entities and Lexical Normalization (Plank et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.583.pdf
- Code
- bplank/DaNplus
- Data
- DaN+