Domain Adaptation for Arabic Machine Translation: The Case of Financial
  Texts
Emad A. Alghamdi Jezia Zakraoui Fares A. Abanmy
Neural machine translation (NMT) has shown impressive performance when
trained on large-scale corpora. However, generic NMT systems have demonstrated
poor performance on out-of-domain translation. To mitigate this issue, several
domain adaptation methods have recently been proposed which often lead to
better translation quality than genetic NMT systems. While there has been some
continuous progress in NMT for English and other European languages, domain
adaption in Arabic has received little attention in the literature. The current
study, therefore, aims to explore the effectiveness of domain-specific
adaptation for Arabic MT (AMT), in yet unexplored domain, financial news
articles. To this end, we developed carefully a parallel corpus for
Arabic-English (AR- EN) translation in the financial domain for benchmarking
different domain adaptation methods. We then fine-tuned several pre-trained NMT
and Large Language models including ChatGPT-3.5 Turbo on our dataset. The
results showed that the fine-tuning is successful using just a few well-aligned
in-domain AR-EN segments. The quality of ChatGPT translation was superior than
other models based on automatic and human evaluations. To the best of our
knowledge, this is the first work on fine-tuning ChatGPT towards financial
domain transfer learning. To contribute to research in domain translation, we
made our datasets and fine-tuned models available at
https://huggingface.co/asas-ai/.