@inproceedings{kchaou-etal-2020-parallel,
    title = "Parallel resources for {T}unisian {A}rabic Dialect Translation",
    author = "Kchaou, Sam{\'e}h  and
      Boujelbane, Rahma  and
      Hadrich-Belguith, Lamia",
    editor = "Zitouni, Imed  and
      Abdul-Mageed, Muhammad  and
      Bouamor, Houda  and
      Bougares, Fethi  and
      El-Haj, Mahmoud  and
      Tomeh, Nadi  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.wanlp-1.18/",
    pages = "200--206",
    abstract = "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 require a huge amount and good management of training data, which represents a challenge in a low-resource setting such as the Tunisian Arabic dialect. In this paper, we 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 Machine Translation (MT) model. The created corpus was used to build a sentence-based translation model. 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."
}