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        <title>What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis</title>
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        <namePart type="given">Xiaolei</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis
%A Huang, Xiaolei
%A May, Jonathan
%A Peng, Nanyun
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F huang-etal-2019-matters
%X 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.
%R 10.18653/v1/D19-1672
%U https://aclanthology.org/D19-1672
%U https://doi.org/10.18653/v1/D19-1672
%P 6395-6401

Markdown (Informal)

[What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis](https://aclanthology.org/D19-1672) (Huang et al., EMNLP-IJCNLP 2019)

• What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (Huang et al., EMNLP-IJCNLP 2019)

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.