High Quality ELMo Embeddings for Seven Less-Resourced Languages
Table 5: The results of NER evaluation task. The scores are macro average F1 scores of the three named entity classes, excluding score for class ”Other”. The columns show fastText, ELMoForManyLangs (EFML), and EMBEDDIA ELMo embeddings.
['Language', 'fastText', 'EFML', 'EMBEDDIA']
We used categorical cross-entropy as a loss function and trained each model for 10 epochs (except Slovenian with EFML embeddings, where we trained for 5 epochs, since it gives a much better score (0.82F1 vs. 0.68F1)). In English, there is still improvement, but a smaller one, in part due to already high performance using fastText embeddings.