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
this https URL

本研究探索了在尚未开发的金融领域中，为阿拉伯机器翻译（AMT）应用特定领域的适应性方法，结果表明只需少量与领域对齐的阿拉伯 - 英语片段，使用预训练的 NMT 和大型语言模型 (包括 ChatGPT-3.5 Turbo) 进行微调能够取得成功，ChatGPT 的翻译质量在自动和人工评估中均优于其他模型。