Amir Mohammad Salehoof


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ParsSimpleQA: The Persian Simple Question Answering Dataset and System over Knowledge Graph
Hamed Babaei Giglou | Niloufar Beyranvand | Reza Moradi | Amir Mohammad Salehoof | Saeed Bibak
Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities

The simple question answering over the knowledge graph concerns answering single-relation questions by querying the facts in the knowledge graph. This task has drawn significant attention in recent years. However, there is a demand for a simple question dataset in the Persian language to study open-domain simple question answering. In this paper, we present the first Persian single-relation question answering dataset and a model that uses a knowledge graph as a source of knowledge to answer questions. We create the ParsSimpleQA dataset semi-automatically in two steps. First, we build single-relation question templates. Next, we automatically create simple questions and answers using templates, entities, and relations from Farsbase. To present the reliability of the presented dataset, we proposed a simple question-answering system that receives questions and uses deep learning and information retrieval techniques for answering questions. The experimental results presented in this paper show that the ParsSimpleQA dataset is very promising for the Persian simple question-answering task.