An Efficient Cross-Lingual BERT Model for Text Classification and Named Entity Extraction in Multilingual Dataset

In recent times, with the rise of the internet, everyone is being bombarded with tons of information and data from various sources like websites, blogs and articles, social media posts and comments, e-news portals etc. Now all these data are mostly unstructured. In this paper, the authors have tried to explore the efficiency of the cross-lingual BERT model i.e. M-BERT for text classification and named entity extraction on multilingual data. The authors have used datasets of three different languages namely: French, German and Portuguese to evaluate the model performance.