Long-Form Open-Domain Question-Answering System Architecture | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Volume 23, Issue 1, March 2023, Page 84-97 PDF (1.56 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2023.185241.1245 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Moataz Mohammed Seliem  1; Salsabil Amin2; M Aref 3
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| 161 Mubarak Saleh St. | ||||
| 2Faculty of Computers and Information Sciences | ||||
| 3Department Computer Science, Faculty of Computer and Information Sciences,Ain Shams University, Cairo, Egypt. | ||||
| Abstract | ||||
| Question Answering is one of the challenging points of research in natural language processing recently. The problem of automating the answering process for the user’s queries became required. So, there were several papers suggested different system architectures for building a question answering systems. In this research paper, we suggest our own system architecture taking into consideration that the input of the system architecture is only the asked question. The suggested system architecture is a long-form open domain question answering that contains mainly two layers. The natural language processing layer which holds the data module and the computing module. This layer is responsible for many operations like pre-processing, preparing, storing the data along with taking the user’s question then providing the suitable answer. The dataset of the proposed system has to be documents annotated with questions and answers extracted from these documents. Also, it has to be in SQUAD format. The computing module is a retriever-reader based deep learning model. This model achieves scores: 67% Recall@100 using dense passage retriever model and 67.7% F1 score for reader model. the Interface layer is the second layer which includes the APIs module and the user-interface module. Finally, we will discuss a real time case study for the system. | ||||
| Keywords | ||||
| Natural Language Processing; Long Form Open Domain Question Answering; Open Domain Question Answering; Semantic Search | ||||
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