Arabic Question Generation Using Transformers

Abstract

After the increased reliance on online education, online assessment became an essential tool for educators to remotely monitor and evaluate students' understanding in order to assist them properly. However, the laborious process of creating exam questions is a challenge for most teachers. Thus, automated Question Generation aims to assist teachers by generating questions from given data. Limited research has been conducted to tackle this issue in the Arabic Language due to the complexity of the language and the limited amount of available Arabic data. This article explores different implementations of the transformer models, that demonstrated their superiority in natural language processing. Three approaches were introduced to tackle this problem with Arabic data using an Arabic-based transformer, an English-based transformer, and a multilingual-based transformer. Each of the fine-tuned models was trained using ARCD, XGLUE, DialectBench, and ArabicQA data sets and evaluated on automatic and manual metrics. Two of the proposed models achieve state-of-the-art results on the Arabic question generation task. The English transformer obtained a ROUGE score of 0.59 on XGLUE, while the Arabic transformer model achieves 0.49 on ARCD. Both of these models demonstrate excellent quality of questions through human-conducted evaluations by achieving low WER and high GC, U, and A scores.