@inproceedings{abdelaziz-etal-2024-llm,
    title = "{LLM}-based {MT} Data Creation: Dialectal to {MSA} Translation Shared Task",
    author = "Abdelaziz, AhmedElmogtaba Abdelmoniem Ali  and
      Elneima, Ashraf Hatim  and
      Darwish, Kareem",
    editor = "Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Mubarak, Hamdy  and
      Ali, Mona  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.osact-1.14",
    pages = "112--116",
    abstract = "This paper presents our approach to the Dialect to Modern Standard Arabic (MSA) Machine Translation shared task, conducted as part of the sixth Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT6). Our primary contribution is the development of a novel dataset derived from The Saudi Audio Dataset for Arabic (SADA) an Arabic audio corpus. By employing an automated method utilizing ChatGPT 3.5, we translated the dialectal Arabic texts to their MSA equivalents. This process not only yielded a unique and valuable dataset but also showcased an efficient method for leveraging language models in dataset generation. Utilizing this dataset, alongside additional resources, we trained a machine translation model based on the Transformer architecture. Through systematic experimentation with model configurations, we achieved notable improvements in translation quality. Our findings highlight the significance of LLM-assisted dataset creation methodologies and their impact on advancing machine translation systems, particularly for languages with considerable dialectal diversity like Arabic.",
}

This paper presents our approach to the Dialect to Modern Standard Arabic (MSA) Machine Translation shared task, conducted as part of the sixth Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT6). Our primary contribution is the development of a novel dataset derived from The Saudi Audio Dataset for Arabic (SADA) an Arabic audio corpus. By employing an automated method utilizing ChatGPT 3.5, we translated the dialectal Arabic texts to their MSA equivalents. This process not only yielded a unique and valuable dataset but also showcased an efficient method for leveraging language models in dataset generation. Utilizing this dataset, alongside additional resources, we trained a machine translation model based on the Transformer architecture. Through systematic experimentation with model configurations, we achieved notable improvements in translation quality. Our findings highlight the significance of LLM-assisted dataset creation methodologies and their impact on advancing machine translation systems, particularly for languages with considerable dialectal diversity like Arabic.
Abdelaziz, AhmedElmogtaba Abdelmoniem Ali Elneima, Ashraf Hatim Darwish, Kareem
LLM-based MT Data Creation: Dialectal to MSA Translation Shared Task