A Novel Framework for Arabic Dialect Chatbot Using Machine Learning. (10th March 2022)

• Record Type: Journal Article
• Authors: Alhassan, Nadrh Abdullah; Saad Albarrak, Abdulaziz; Bhatia, Surbhi; Agarwal, Parul
• Other Names: Sah Tyagi Sumarga Kumar Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-03-10
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/1844051
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : With the advent of artificial intelligence and proliferation in the demand for an online dialogue system, the popularity of chatbots is growing on various industrial platforms. Their applications are getting widely noticed with intelligent tools as they are able to mimic human behavior in natural languages. Chatbots have been proven successful for many languages, such as English, Spanish, and French, over the years in varied fields like entertainment, medicine, education, and commerce. However, Arabic chatbots are challenging and are scarce, especially in the maintenance domain. Therefore, this research proposes a novel framework for an Arabic troubleshooting chatbot aiming at diagnosing and solving technical issues. The framework addresses the difficulty of using the Arabic language and the shortage of Arabic chatbot content. This research presents a realistic implementation of creating an Arabic corpus for the chatbot using the developed framework. The corpus is developed by extracting IT problems/solutions from multiple domains and reliable sources. The implementation is carried forward towards solving specific technical solutions from customer support websites taken from different well-known organizations such as Samsung, HP, and Microsoft. The claims are proved by evaluating and conducting experiments on the dataset by comparing with the previous researches done in this field using different metrics. Further, the validations are well presented by the proposed system that outperforms the previously developed different types of chatbots in terms of several parameters such as accuracy, response time, dataset data, and solutions given as per the user input.