DziriBERT: a Pre-trained Language Model for the Algerian Dialect
This addresses the problem of limited NLP resources for Algerian dialect speakers, though it is incremental as it applies an existing method to a new language.
The authors tackled the lack of pre-trained language models for the low-resource Algerian dialect by collecting over one million Algerian tweets and pre-training DziriBERT, which outperforms existing models, especially for Roman script, using only 150 MB of data compared to hundreds of GB for others.
Pre-trained transformers are now the de facto models in Natural Language Processing given their state-of-the-art results in many tasks and languages. However, most of the current models have been trained on languages for which large text resources are already available (such as English, French, Arabic, etc.). Therefore, there are still a number of low-resource languages that need more attention from the community. In this paper, we study the Algerian dialect which has several specificities that make the use of Arabic or multilingual models inappropriate. To address this issue, we collected more than one million Algerian tweets, and pre-trained the first Algerian language model: DziriBERT. When compared with existing models, DziriBERT achieves better results, especially when dealing with the Roman script. The obtained results show that pre-training a dedicated model on a small dataset (150 MB) can outperform existing models that have been trained on much more data (hundreds of GB). Finally, our model is publicly available to the community.