  Question answering(QA) is one of the most challenging yet widely investigated
problems in Natural Language Processing (NLP). Question-answering (QA) systems
try to produce answers for given questions. These answers can be generated from
unstructured or structured text. Hence, QA is considered an important research
area that can be used in evaluating text understanding systems. A large volume
of QA studies was devoted to the English language, investigating the most
advanced techniques and achieving state-of-the-art results. However, research
efforts in the Arabic question-answering progress at a considerably slower pace
due to the scarcity of research efforts in Arabic QA and the lack of large
benchmark datasets. Recently many pre-trained language models provided high
performance in many Arabic NLP problems. In this work, we evaluate the
state-of-the-art pre-trained transformers models for Arabic QA using four
reading comprehension datasets which are Arabic-SQuAD, ARCD, AQAD, and
TyDiQA-GoldP datasets. We fine-tuned and compared the performance of the
AraBERTv2-base model, AraBERTv0.2-large model, and AraELECTRA model. In the
last, we provide an analysis to understand and interpret the low-performance
results obtained by some models.
