## 1 Introduction  
Successive advances in information and communication technology (ICT) have allowed unprecedented progress in the domain of crowdsourcing platforms. The basic idea behind crowdsourcing platforms is that complex tasks can be disintegrated into a great number of smaller pieces and delegated to a great number of individuals to address them, thus enabling a collective, but not necessarily conscious and optimally coordinated effort of complex problem solving. Critical in the debate on crowdsourcing platforms is the notion of task delegation, i.e., human intelligence tasks (HITs). HITs are a form of micro-work [1]. Indeed, individuals performing such tasks are usually paid or remunerated for their input in alternative ways. Importantly, the great number of individuals dealing with the small tasks assigned to them use their cognitive skills to solve problems. The case of crowdsourcing platforms suggests, however, that micro-work is a necessary structural component of contemporary artificial intelligence production processes [1]. Research on natural language processing (NLP), artificial intelligence (AI), and related applications and their uses, is continuing to proliferate [2; 3]. However, if the development of NLP applications is to be maintained, language differences and culturally determined language specificity need to be brought into the analysis and application development process. This means that custom-made approaches and solutions to NLP are required to support different languages, such as Arabic, Urdu, and Persian [4]. NLP crowdsourcing platforms attest to that. Literature suggests that considerable progress has been attained in this regard, especially in relation to English and Chinese [5; 6]. As a result, relatively efficient and accurate crowdsourcing platforms have been created that, at this stage, support diverse forms of research requiring data collection [7; 8; 9; 10]. This includes academic research, market and consumer satisfaction surveys, etc. Thanks to crowdsourcing platforms, the tasks that these activities require can be performed quickly and cost-effectively, while also allowing the collection of good quality data. With regards to NLP, crowdsourcing platforms prove useful in a variety of research projects that require text summarization, machine translation, and speech recognition. To this end, the following crowdsourcing platforms have been commonly used: Amazon's Mechanical Turk [7], CrowdFlower [8], Lionbridge [9], Prolific Academic, and others [1; 10]. Although crowdsourcing platforms have a great potential for research, each of these platforms has certain limitations. For instance, Amazon Mechanical Turk is difficult to employ outside of the United States. Sometimes, the complexity of specific languages limits the pool of participants involved in a crowdsourcing activity. This would be the case of the German ClickWorker crowdsourcing platform [11], or the Chinese Zhubajie/Wittart crowdsourcing platform used as a benchmark for research in China [12]. Some progress has been attained with regards to NLP research oriented on the Arabic language [13; 14; 15]. Nevertheless, even though a few researchers sought to develop their own crowdsourcing platforms, the tendency was to employ the existing platforms, such as Amazon Mechanical Turk and CrowdFlower. The objective of this paper is to support NLP Arabic-based research. To this end, a new crowdsourcing platform, named Tashkeel, is proposed and elaborated. The name of the platform is inspired by the eight main diactitics that transform a word written with an Arabic letter into a vast range of forms and meaning. The power of those eight marks, including Fathah, Kasrah, Dhammah, Sukun, Shaddah, Tanwin (Fath), Tanwin (Kasr), and Tanwin (Dham), in enriching the Arabic language inspired us to give the platform this name, in order to have the same impact on NLP Arabic research. The argument is structured as follows. First, a review of the crowdsourcing platforms' scene employed for studies related to the Arabic language is presented.