Multilingual Transformers for Named Entity Recognition

Different methods for automatic named entity recognition (NER) have been researched for many years. Today, the most common technique for training named entity recognition models is a fine-tuning of large pre-trained language models. In this paper, we investigate the performance of various multilingual NER models in the state-of-the-art natural language processing frame-work Flair and compare them against the multilingual NER solution of the MAPA anonymization toolkit and BERT multilingual model, fine-tuned for NER. We demonstrate that in multilingual settings the best results could be achieved with fine-tuned XLM-R model, while in the case of Latvian (monolingual settings), the more targeted LitLat BERT model leads to the best results.