Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural language tasks. One of the most prominent pre-trained language models is BERT (Bi-directional Encoders for Transformers) , which was released as an English as well as a multilingual version. Although multilingual BERT performs well on many tasks, recent studies showed that BERT models trained on a single language significantly outperform the multilingual results . Training a Dutch BERT model thus has a lot of potential for a wide range of Dutch NLP tasks. While previous approaches have used earlier implementations of BERT to train their Dutch BERT, we used RoBERTa, a robustly optimized BERT approach, to train a Dutch language model called RobBERT. We show that RobBERT improves state of the art results in Dutch-specific language tasks, and also outperforms other existing Dutch BERT-based models in sentiment analysis . These results indicate that RobBERT is a powerful pre-trained model for fine-tuning for a large variety of Dutch language tasks. We publicly release this pre-trained model in hope of supporting further downstream Dutch NLP applications.
Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural language tasks. One of the most prominent pre-trained language models is BERT , which was released as an English as well as a multilingual version. Although multilingual BERT performs well on many tasks, recent studies show that BERT models trained on a single language significantly outperform the multilingual version . Training a Dutch BERT model thus has a lot of potential for a wide range of Dutch NLP tasks. While previous approaches have used earlier implementations of BERT to train a Dutch version of BERT, we used RoBERTa, a robustly optimized BERT approach, to train a Dutch language model called RobBERT. We measured its performance on various tasks as well as the importance of the fine-tuning dataset size. We also evaluated the importance of language-specific tokenizers and the model's fairness. We found that RobBERT improves state-of-the-art results for various tasks, and especially significantly outperforms other models when dealing with smaller datasets . These results indicate that it is a powerful pre-trained model for a large variety of Dutch language tasks. The pre-trained and fine-tuned models are publicly available to support further downstream Dutch NLP applications.
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