# Domain Adaptation for Arabic Machine Translation: Financial Texts as a Case Study
###### Abstract  
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 a parallel corpus for Arabic-English (AR-EN) translation in the financial domain to benchmark 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 fine-tuning pre-trained NMT models on a few well-aligned in-domain AR-EN segments led to noticeable improvement. The quality of ChatGPT translation was superior to 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.  
machine translation; Arabic MT; domain adaptation; financial domain +
Footnote †: journal: Medical  
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techniques [12], and mixed fine-tuning [6]. However, domain robustness remains an unsolved problem and there is a need for further research in this area [13]. This is especially true for the Arabic language. Existing domain adaptation research has only focused on news [14] and medical [7] domains, no prior study, to the best of our knowledge, has been conducted on the financial domain.  
To alleviate the issue of translation mismatch related to out-of-domain texts, the authors in [14] studied the performance of NMT systems under morphology-based and frequency-based tokenization schemes and BPE on in-domain data. They evaluated their best-performing models on out-of-domain data yielding significant improvements of 37.96% in BLEU score [15]. Ref. [7] proposed a method for domain-specific data augmentation for MT to tackle the issue with a small bilingual dataset. They employed mixed fine-tuning to train models that significantly improve the translation of in-domain texts. Their method achieved improvements of approximately 5-6 BLEU and 2-3 BLEU, respectively, on the Arabic-to-English and English-to-Arabic language pairs.  
While a lot of research in domain adaptation in MT for other language pairs like [6], ref. [16] exists which focuses on synthetic data generation and multiple other techniques like checkpoint averaging [6], only one work [7] investigated the same for AMT, but only for a medical domain. Therefore, this research aims to fill the gap in evaluating different MT settings and investigate domain adaption for financial texts. Our contributions are the following:  
* We introduce the first AR-EN parallel corpus in the financial domain.
* We compare the effectiveness of different adaption methods and data augmentation approaches for limited domain data.
* We fine-tuned several models and made them publicly available to the research community at [[https://huggingface.co/asas-ai/](https://huggingface.co/asas-ai/)]([https://huggingface.co/asas-ai/](https://huggingface.co/asas-ai/)) (accessed on 20 July 2024).
* Our work is the first to fine-tune the GPT3.5 model and evaluate its capability for domain adaption.