Question-Aware Deep Learning Model for Arabic Machine Reading Comprehension

Machine reading comprehension is one of the most long-standing challenges in artificial intelligence. It is a subtask question-answering system that aims to find a text span in a reading context that answers a question. It is the core of several applications, such as customer service systems and chatbots. However, However, few studies have targeted machine reading comprehension of Arabic text. Besides, The existing models generate a context representation independently from the question. As an analogy to some people’s comprehension styles who believe that by reading the question, they gain a better understanding of the reading passage, we present a question-aware neural machine reading comprehension model for Arabic. Our model extracts a representation of the context by incorporating the question using several bidirectional attention units to achieve various levels of question-centered context understanding. Our experimental evaluation shows that our model outperforms strong machine reading comprehension baselines, including DrQA and QaNET models applied to Arabic, by a significant margin with an F1 score of 59.6%.