Towards Lingua Franca Named Entity Recognition with BERT

Understand

Information extraction is an important task in NLP, enabling the automatic extraction of data for relational database filling.

• Historically, research and data was produced for English text, followed in subsequent years by datasets in Arabic, Chinese (ACE/OntoNotes), Dutch, Spanish, German (CoNLL evaluations), and many others.
• The natural tendency has been to treat each language as a different dataset and build optimized models for each.
• In this paper we investigate a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language.