Parallel resources for Tunisian Arabic dialect translation

The difficulty of processing dialects is clearly observed in the high cost of building representative corpus, in particular for machine translation. Indeed, all machine translation systems requirea huge amount and good management of training data, which represents a challenge in a low-resource setting such as the Tunisian Arabic dialect. The paper present a data augmentation technique to create a parallel corpus for Tunisian Arabic dialect written in social media and standard Arabic in order to build a MachineTranslation model. The created corpus was used to build a sentence-based translation model for testing data. This model reached a BLEU score of 15.03%on a test set, while it was limited to 13.27% utilizing the corpus without augmentation.