What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis
Table 2: F1 score comparisons of cross-lingual models on Spanish, Dutch and German. The “*” indicates the model uses the Wikipedia resources. The “+” means training model by multiple language resources.
['Models', 'Spanish', 'Dutch', 'German']
Moreover, the two neural model methods exceed the performance of the other models. These results suggest the effectiveness of neural cross-lingual transfer through multi-lingual embedding space, and show competitive results of our proposed methods. The observation indicates that entities longer than two tokens are more difficult to infer. This might encourage us to balance the weight of long entities in our current evaluation method which ignores entity length when datasets have high volumes of long entities.