ARBML : Democratizing A rabic Natural Language Processing Tools (2020)

Abstract: Automating natural language understanding is a lifelong quest addressed for decades. With the help of advances in machine learning and particularly, deep learning, we are able to produce state of the art models that can imitate human interactions with languages. Unfortunately, these advances are controlled by the availability of language resources. Arabic advances in this field , although it has a great potential, are still limited. This is apparent in both research and development. In this paper, we showcase some NLP models we trained for Arabic. We also present our methodology and pipeline to build such models from data collection, data preprocessing, tokenization and model deployment. These tools help in the advancement of the field and provide a systematic approach for extending NLP tools to many languages.

## introduction arabic language is a widely used language. it is the sixth most spoken language in the world @xcite . it also has a noticeable influence on many other languages around the globe. compared to english, arabic is morphologically a very rich language (habash, 2010) with relatively complex grammar and cursive script including the use of diacritics. diacritics are special characters added to arabic writing to replace the absence of short vowels. moreover, arabic has a variety of dialects that may greatly differ in style and grammar. arabic content on the web is vastly emerging with great diversity in style and subjects, written in many dialects. this opportunity opens doors for research to hone machine capabilities to automate language understanding and comprehension. however, arabic inherent characteristics makes it difficult to resolve and require linguistic expertise. natural language processing is gaining a lot of attractions within the research community. the aim is to create machines that can replicate or exceed human language understanding. on another perspective, a lot of effort is invested to develop software applications to port research advances to industry. another effort is also directed to facilitate researchers job by automating routine workflows, cleaning and preprocessing, for example. some examples of this are huggingface, allennlp and flare. most of these tools are designed to work for english or generalize the pipeline to work for other languages. arabic, although it is not as popular as other languages tools, also has some contributions, but in the linguistics part only. some promising examples are madamira @xcite , farasa @xcite , adawat (zerrouki, 2020) and camel nlp @xcite . these tools address a large spectrum of nlp tasks for arabic like segmentation, part of speech tagging, named entity recognition, diacritizatoin and grammatical analysis. however, most of these tools are not using the recent advances in nlp. unfortunately, in the arabic community, open source contribution is not widely accepted. this is due to the copyrights restrictions made by authors as some of these tools are not licensed for commercial use. although, the source code can be delivered on demand, this mechanism is still not development friendly with unclear methodology and processes to version control and collaboration. in this paper, we introduce our contribution to the arabic language open source community. we present a collection of models that can be utilized and improved to solve a wide variety of many natural language processing tasks. moreover, we introduce three libraries for scrapping, cleaning and tokenization. we also provide notebooks that can be easily used to replicate our experiments. we provide a flexible code design that can be implemented and extended to other languages. ## design we created arbml in the hope of democratizing arabic natural language processing by creating a set of demos as well as tools for making it easy to use for novice users, developers and researchers. we revise the nlp pipeline and make it suitable for arabic as indicated in figure 1 . we provide datasets, preprocessors, tokenizers and models. furthermore, we host notebooks that can replicate our experiments and help users to understand how to do each task. in the next few sections we explain our main tools. ## end-user experience arbml provides a solid contribution from two main perspectives: ## conclusion and future plans recently, many nlp tools have been developed but they only focus on english. in this work we showcased arbml which is a set of tools that make arabic nlp easily accessible through different interfaces. we target the nlp pipeline starting from scrapping datasets, preprocessing, tokenization to training and deployment. we focused on making the design of our tools language-agnostic and hence can be extended to many other languages, given we change the morphological aspects. we collected many datasets that can be easily used by researchers to develop new models. we also designed three libraries tnqeeb, tnkeeh and tkseem which can be easily utilized by developers to develop tools to support arabic nlp. the tools utilize the morphological nature of arabic to provide different functionalities that are unique for arabic. we plan to add many other models and make them easily accessible through the browser. mainly, our next step is to tackle more advanced models like transformers @xcite . furthermore, we want to apply different techniques like quantization and distillation to make the models available in the browser. moreover, we would like to focus on light models like mobilebert @xcite , retrain it for arabic and make it readily usable in the browser.