CamemBERT: a Tasty French Language Model
This addresses the limited practical use of language models in languages other than English, providing a high-performing monolingual solution for French NLP tasks.
The authors tackled the lack of monolingual pretrained language models for non-English languages by training a French Transformer-based model, CamemBERT, which achieved state-of-the-art results on four downstream tasks, with a key finding that a small 4GB web-crawled dataset performed as well as larger 130+GB datasets.
Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models --in all languages except English-- very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks.