  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.
