Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Table 2: Comparison of different ways of using bilingual word embeddings, within our method (NER F1).
['Model', 'Spanish', 'Dutch', 'German']
The “common space” variant performs the worst by a large margin, confirming our hypothesis that discrepancy between the two embedding spaces harms the model’s ability to generalize. From the comparison between the “replace” and “translation,” we observe that having access to the target language’s character sequence helps performance, especially for German, perhaps due in part to its capitalization patterns, which differ from English. In this case, we have to lower-case all the words for character inputs in order to prevent the model from overfitting the English capitalization pattern.