Papers › Tuning Multilingual Transformers for Named Entity Recognition on Slavic Languages
Tuning Multilingual Transformers for Named Entity Recognition on Slavic Languages
Mikhail Arkhipov, Maria Trofimova, Yuri Kuratov, Alexey Sorokin
Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish. We solve this task using the BERT model. We use a hundred languages multilingual model as base for transfer to the mentioned Slavic languages. Unsupervised pre-training of the BERT model on these 4 languages allows to significantly outperform baseline neural approaches and multilingual BERT. Additional improvement is achieved by extending BERT with a word-level CRF layer. Our system was submitted to BSNLP 2019 Shared Task on Multilingual Named Entity Recognition and took the 1st place in 3 competition metrics out of 4 we participated in. We open-sourced NER models and BERT model pre-trained on the four Slavic languages.
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