Abstract
Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages, it is unclear what knowledge is transferred. In this paper, we first propose a simple and efficient neural architecture for cross-lingual NER. Experiments show that our model achieves competitive performance with the state-of-the-art. We further explore how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embedding. Our results shed light on future research for improving cross-lingual NER.- Anthology ID:
- D19-1672
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6395–6401
- Language:
- URL:
- https://aclanthology.org/D19-1672
- DOI:
- 10.18653/v1/D19-1672
- Cite (ACL):
- Xiaolei Huang, Jonathan May, and Nanyun Peng. 2019. What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6395–6401, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (Huang et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D19-1672.pdf