Open-domain question answering (OpenQA) is one of the most challenging yet widely investigated problems in natural language processing. It aims at building a system that can answer any given question from large-scale unstructured text or structured knowledge-base. To solve this problem, researchers traditionally use information retrieval methods to retrieve the most relevant documents and then use answer extractions techniques to extract the answer or passage from the candidate documents. In recent years, deep learning techniques have shown great success in OpenQA by using dense representation for document retrieval and reading comprehension for answer extraction. However, despite the advancement in the English language OpenQA, other languages such as Arabic have received less attention and are often addressed using traditional methods. In this paper, we use deep learning methods for Arabic OpenQA. The model consists of document retrieval to retrieve passages relevant to a question from large-scale free text resources such as Wikipedia and an answer reader to extract the precise answer to the given question. The model implements dense passage retriever for the passage retrieval task and the AraELECTRA for the reading comprehension task. The result was compared to traditional Arabic OpenQA approaches and deep learning methods in the English OpenQA. The results show that the dense passage retriever outperforms the traditional Term Frequency-Inverse Document Frequency (TF-IDF) information retriever in terms of the top-20 passage retrieval accuracy and improves our end-to-end question answering system in two Arabic question-answering benchmark datasets.
Keywords: Arabic open domain question answering; Dense information retrieval approach; Transformer-based models for question answering.
© 2022 Alsubhi et al.