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Text-based multitype question answering is one of the research hotspots in the field of reading comprehension models. Multitype reading comprehension models have the characteristics of shorter time to propose, complex components of relevant corpus, and greater difficulty in model construction. There are relatively few research works in this field. Therefore, it is urgent to improve the model performance. In this paper, a text-based multitype question and answer reading comprehension modeldoi:10.1155/2021/8810366 pmid:33679967 pmcid:PMC7910065 fatcat:rzb7oyybebcd3ppfrxz4jjqsza