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arXiv · 1909.03598

What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis

Xiaolei Huang·Jonathan May·Nanyun Peng
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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 to low-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 analyze how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embeddings, and investigate how model performance varies across entity lengths. Finally, we conduct a case-study on a non-Latin language, Bengali, which suggests that leveraging knowledge from Wikipedia will be a promising direction to further improve the model performances. Our results can shed light on future research for improving cross-lingual NER.

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Xiaolei Huang, Jonathan May, Nanyun Peng. 2019-09-09. What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis. https://arxiv.org/abs/1909.03598

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