# Domain Adaptation for Arabic Machine Translation: Financial Texts as a Case Study
## 2 Background
### Domain-Adaptation in Arabic MT  
The development of Arabic MT systems has gone through different stages, including rule-based systems [41; 42], statistical MT [43], and more recently neural MT systems [44]. Ref. [45] conducted a comprehensive survey of Arabic MT systems and the unique challenges in Arabic MT.  
Arabic is one of the official six languages adopted by the United Nations and it is spoken by 400 million people in the Middle East, North Africa, and many other parts of the world. Arabic is a Semitic language and it is notoriously difficult for MT due to its linguistic characteristics [45; 46]. First, Arabic has a rich and complex morphology which is substantially different from English or other western languages [47]. Second, Arabic has long and short vowels. While the long vowels are represented by letters, the short vowels are marked by diacritic signs placed above or below the letters. However, the use of diacritic signs is not compulsatory in Arabic and hence they are rarely used in informal writing. Therefore, it is hard to identify the correct sense of a word, especially when sufficient context is not provided. Third, variation among different Arabic dialects has always been problematic for AMT. Furthermore, the Arabic language used in social media varies considerably from Modern Standard Arabic (MSA). These aspects of the Arabic language pose series challenges for Arabic MT.  
In addition to the aforementioned issues, there is a lack of high-quality parallel corpora of sufficient size for training or fine-tuning Arabic MT for different domains. It is commonly known that NMT systems do not perform well in domain-specific translation, especially in low-resource languages [1]. Addressing these challenges, some researchers have turned to domain adaption methods to develop domain-specific Arabic MT systems. For example, ref. [7] proposed the use of pre-trained LMs and back-translation for domain-specific data augmentation for MT.  
Furthermore, current Arabic MT research has primarily focused on the translation of limited domains such as news and official texts, whilst few attempts focus on domain-specific translation such as medical domain [48]. Specifically, most of the parallel data available to the researcher was limited to texts produced by international organizations, and parliamentary debates [33]. Unfortunately, existing single-domain AMT methods do not work well for multiple domains. Thus, multi-domain NMT approaches are more in demand to tackle this limitation.  
To recap, previous research has shown that domain adaptation leads to better translation quality than general NMT. While there has been considerable progress in general MT from Arabic to English [5,49], less work has been conducted on adapting models to specific domains such as medical domains [7], but to the best of our knowledge no work investigated the adaptability in financial texts. Since there is relatively little work on Arabic domain adaptation, the primary objective of this research is to explore the different effectiveness of domain translation methods, a yet unexplored domain, financial domain. To this end, this work aims to fine-tune several Transformer NMT models and LLM and perform cross-domain testing and evaluation to gain some insights into model robustness against domain changes.