Pre-trained Transformer-Based Approach for Arabic Question Answering : A Comparative Study
This work addresses the scarcity of research in Arabic QA by providing a comparative analysis of models, but it is incremental as it applies existing methods to Arabic data without introducing new techniques.
The study tackled the problem of Arabic question answering by evaluating pre-trained transformer models on four datasets, finding that AraELECTRA achieved the best performance with an F1 score of 86.7% on Arabic-SQuAD, while some models showed low performance due to dataset-specific challenges.
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.