ArRASA: Channel Optimization for Deep Learning-Based Arabic NLU Chatbot Framework

Abstract

Since the introduction of deep learning-based chatbots for knowledge services, many research and development efforts have been undertaken in a variety of fields. The global market for chatbots has grown dramatically as a result of strong demand. Nevertheless, open-domain chatbots’ limited functional scalability poses a challenge to their implementation in industries. Much work has been performed on creating chatbots for languages such as English, Chinese, etc. Still, there is a need to develop chatbots for other languages such as Arabic, Persian, etc., as they are widely used on the Internet today. In this paper, we introduce, ArRASA as a channel optimization strategy based on a deep-learning platform to create a chatbot that understands Arabic. ArRASA is a closed-domain chatbot that can be used in any Arabic industry. The proposed system consists of four major parts. These parts include tokenization of text, featurization, intent categorization and entity extraction. The performance of ArRASA is evaluated using traditional assessment metrics, i.e., accuracy and F1 score for the intent classification and entity extraction tasks in the Arabic language. The proposed framework archives promising results by securing 96%, 94% and 94%, 95% accuracy and an F1 score for intent classification and entity extraction, respectively.