Towards Lingua Franca Named Entity Recognition with BERT
Table 1: Single and multi language F1 on CoNLL’02, CoNLL’03. Flair-ML is the system described in [1], trained multilingually, available from [2].
['System', 'English', 'Spanish', 'German', 'Dutch']
For both CoNLL and OntoNotes, the model that was trained on all the languages (BERT-ML) performs better than the model trained on one language at a time (BERT-SL), in all cases, showing that the BERT-ML model is able to benefit from information coming from all the languages to improve each individual language, validating our first two hypotheses: that we can effectively build a multi-language model, and that it performs better than individual models. Relative to other published results, the Bert-ML CoNLL model obtains SotA results for Spanish and Dutch and is ranked second for German, and the Bert-ML OntoNotes model obtains SotA for Chinese and Arabic and is ranked third for English.