The scope of Artificial Intelligence in Arabic language: Opportunities, challenges and use cases

There is a significant lag in the total number of Arabic speakers (5%) vs Arabic content online (1%). The Arabic texts and data publicly available online are minuscule compared to the abundance of available English texts. Thus, it is hardly a surprise that there is insufficient training data to develop accurate and reliable AI systems. There are fewer annotated datasets available for Arabic NLP which hinders the development of advanced models and AI.

Arabic has a rich morphology. In addition to being inflected for number, gender, voice and case, its distinctive word formation and grammatical rules pose unique challenges for translation systems. An NLP model must be trained with high-quality datasets (already difficult to come by) to recognise these inflections and understand their impact on a sentence's meaning. For example, when changing the subject from "I" to "we," the verb "read" also changes to match the plural subject.

Arabic has roughly 25 dialects across three main versions of Arabic: Quranic or Classical, Modern Standard and Colloquial. These dialects differ significantly from the Modern Standard Arabic used in formal writing. While most models tend to perform well in Modern Standard Arabic (MSA), they tend to struggle with dialects. This poses a serious challenge because according to reports, many business owners throughout the Arabic-speaking world prefer to have AI models available in Arabic dialects. This is because they use these dialects more commonly than Modern Standard Arabic to conduct business with their customers.

The language’s highly complex sentence structure makes parsing difficult. It is difficult for NLP systems to understand the relationships between words and phrases. Unlike English, which follows a relatively fixed subject-verb-object word order, Arabic allows for a more flexible word order, such as verb-subject-object and subject-object-verb.

Arabic is a right-to-left language and the order of the words in a sentence is reversed compared to left-to-right languages like English. Therefore, reversing the text could change the meaning of the sentences and produce incorrect results. On the other hand, if one doesn't reverse the text, the language model may not be able to learn the correct relationships between the words and perform poorly on real-world data. Thus, Arabic demands special handling in text processing and rendering.

Arabic LLMs often miss cultural subtexts because they are trained on adequate datasets consisting of different accents and dialects. These LLMs follow the “standard” Arabic for a “general” audience which causes discrepancies.

To overcome this gap, the datasets must include a variety of audio and video samples from different regions. These can include movies, television, literature, and other forms of media.

Doing this can help the models generate more accurate responses and reflect the cultural and linguistic nuances of different Arabic-speaking regions. It helps the model understand local dialects, expressions, and cultural references, making it more accurate and relevant to those areas. 

There are already ongoing efforts in the Middle East to train AI models to understand and respond accurately in various Arabic dialects rather than just Modern Standard Arabic. Their purpose is to make Arabic LLMs more inclusive and accessible to the greater public.

The public sector’s involvement in the development of AI strategies is critical to further advancements. By investing in AI research and collaborating with international institutions, governments can nurture local talent and attract global expertise. Establishing robust legal frameworks and ethical guidelines also ensures that the development is responsible and inclusive, and addresses key concerns such as data privacy and security.

The Emirati and Saudi governments have taken solid steps in this regard. They have appointed active government offices to pioneer innovation in the AI space. For instance, The UAE Council for Artificial Intelligence and Blockchain, formed in 2018, has been tasked with proposing policies to create an AI-friendly ecosystem. Similarly, NCAI is the innovation arm of the Saudi Data and Artificial Intelligence Authority. One of its core focuses is nationally strategic Arabic Language AI products and services while investing heavily in building Arabic-focused reusable foundational pre-trained models for language and speech.

Arabic LLMs can be applied to extract text from complex handwritten documents, such as bank cheques, to improve accuracy and efficiency in document processing. We are currently developing an Arabic Handwriting Bank cheque extractor that uses advanced Deep Learning and NLP techniques to automate the extraction of details like names, dates, amounts, and signatures from cheques written in Arabic. This significantly speeds up the review process and reduces manual work in financial institutions.

Processing financial documents like invoices, cheques, and delivery notes is a critical function in business operations, but it is often plagued by inefficiencies, errors, and high costs. Traditional methods involve manual data entry, cross-verification, and extensive paper trails, leading to delayed payments and cash flow issues. To overcome this challenge, we have developed Arabic LLMs to speed up the process of financial document review.

The system works by extracting and analysing key information from large volumes of documents, such as Arabic invoices. By utilising advanced Deep Learning models and NLP frameworks like PyTorch, we have developed systems that process Arabic documents more quickly, improving workflow efficiency. These models are trained to handle both printed and handwritten Arabic texts.

Deep Learning models specialised for Arabic can enhance NLP applications such as text summarisation, translation, and question-answering systems. Our team has integrated Deep Learning NLP models that are specifically fine-tuned for the complexities of Arabic, focusing on accurately interpreting Arabic morphology and dialect variations. This is crucial in sectors like banking, where precise interpretation of terms is necessary for legal and compliance reasons.

Arabic is a right-to-left language and the order of the words in a sentence is reversed compared to left-to-right languages like English. Therefore, reversing the text could change the meaning of the sentences and produce incorrect results. On the other hand, if one doesn't reverse the text, the language model may not be able to learn the correct relationships between the words and perform poorly on real-world data. Thus, Arabic demands special handling in text processing and rendering.