OSACT 2024 Task 2: Arabic Dialect to MSA Translation

Hanin Atwany, Nour Rabih, Ibrahim Mohammed, Abdul Waheed, Bhiksha Raj

Abstract
We present the results of Shared Task “Dialect to MSA Translation”, which tackles challenges posed by the diverse Arabic dialects in machine translation. Covering Gulf, Egyptian, Levantine, Iraqi and Maghrebi dialects, the task offers 1001 sentences in both MSA and dialects for fine-tuning, alongside 1888 blind test sentences. Leveraging GPT-3.5, a state-of-the-art language model, our method achieved the a BLEU score of 29.61. This endeavor holds significant implications for Neural Machine Translation (NMT) systems targeting low-resource langu ages with linguistic variation. Additionally, negative experiments involving fine-tuning AraT5 and No Language Left Behind (NLLB) using the MADAR Dataset resulted in BLEU scores of 10.41 and 11.96, respectively. Future directions include expanding the dataset to incorporate more Arabic dialects and exploring alternative NMT architectures to further enhance translation capabilities.
Anthology ID:
2024.osact-1.12
Volume:
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
Editors:
Hend Al-Khalifa, Kareem Darwish, Hamdy Mubarak, Mona Ali, Tamer Elsayed
Venues:
OSACT | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
98–103
Language:
URL:
https://aclanthology.org/2024.osact-1.12
DOI:
Bibkey:
Cite (ACL):
Hanin Atwany, Nour Rabih, Ibrahim Mohammed, Abdul Waheed, and Bhiksha Raj. 2024. OSACT 2024 Task 2: Arabic Dialect to MSA Translation. In 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, pages 98–103, Torino, Italia. ELRA and ICCL.
Cite (Informal):
OSACT 2024 Task 2: Arabic Dialect to MSA Translation (Atwany et al., OSACT-WS 2024)
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