What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis
Table 3: F1 scores of different ablation analyses, compared to our full model.. “Shuffle” means the training data is shuffled. “Word-only” means our proposed model is only fed bilingual word embeddings, and “Char-only” means the model only receives character embeddings as input.
['Models', 'Spanish', 'Dutch', 'German']
Syntax analysis. Different languages might not share the same syntax structures. The neural model learns sequential information from the source language and applies the learned knowledge on the target language. The importance of this sequential information is unknown. We shuffle the sentences of training data while keeping the internal order of named entities unchanged. The results show that after shuffling, the performances of our model decrease and the decreases vary among different languages. This suggests the importance of sequential information for those languages under the cross-lingual settings. Embeddings analysis. We train our proposed models with both character and bilingual word embeddings, however, how the model values different embeddings is unknown. We feed the model with either bilingual word or character embeddings. The results suggest that the aligned bilingual embeddings are more important than the character embeddings for the three languages.