AraScore: A deep learning-based system for Arabic short answer scoring
Omar Nael, Youssef ELmanyalawy, Nada Sharaf
Abstract
In the past years, Arabic NLP has been significantly lagging behind its English counterpart, but recent advancements in Natural Language Processing have made it possible for Arabic to catch up and show promising results for a multitude of tasks. Complex tasks such as short answer scoring, have been widely researched mainly for English, leveraging machine learning and state-of-the-art deep learning techniques. In this paper, we introduce the first deep learning-based system for Arabic short answer scoring, in efforts to provide a reliable system that can help teachers in the Arab world better utilize their time in other teaching activities that would elevate the quality of learning in the region. We empirically study different techniques and propose the best performing system based on our results, where we have achieved state-of-the-art performance, achieving a QWK score of 0.78 and showing how powerful and robust recent Arabic NLP tools have become.