@inproceedings{huang-etal-2019-matters,
title = "What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis",
author = "Huang, Xiaolei and
May, Jonathan and
Peng, Nanyun",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/D19-1672/",
doi = "10.18653/v1/D19-1672",
pages = "6395--6401",
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."
}
Markdown (Informal)
[What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis](https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/D19-1672/) (Huang et al., EMNLP-IJCNLP 2019)
ACL