MTQA: Text-Based Multitype Question and Answer Reading Comprehension Model

Deguang Chen, Ziping Ma, Lin Wei, Jinlin Ma, Yanbin Zhu, Paolo Gastaldo
2021 Computational Intelligence and Neuroscience  
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 model
more » ... is proposed. The model is based on a multilayer transformer encoding and decoding structure. In the decoding structure, the headers of the answer type prediction decoding, fragment decoding, arithmetic decoding, counting decoding, and negation are added for the characteristics of multiple types of corpora. Meanwhile, high-performance ELECTRA checkpoints are employed, and secondary pretraining based on these checkpoints and an absolute loss function are designed to improve the model performance. The experimental results show that the performance of the proposed model on the DROP and QUOREF corpora is better than the best results of the current existing models, which proves that the proposed MTQA model has high feature extraction and relatively strong generalization capabilities.
doi:10.1155/2021/8810366 pmid:33679967 pmcid:PMC7910065 fatcat:rzb7oyybebcd3ppfrxz4jjqsza