Machine Reading Comprehension Based on Shared Structure Information between Naming and Telling
The machine reading comprehension(MRC)task challenges the machine's ability to understand natural language by asking the machine to answer questions in a given context.To improve the accuracy of answer extraction involving the crossing of punctuation sentences and long-distance correlation of answer elements,clue elements and question elements,this paper proposes to model the long-distance relationship between punctuation sentences,and complement the missing components by shared structure.A machine reading comprehension model is implemented by integrating the Naming-Telling structure information.The experimental results on the public data set CMRC2018 show that the proposed method achieves an increase of 2.4％in F1-value and 6％in EM value compared with the baseline model.
machine reading comprehensionnaming-telling structureattentionpretraining language model
万方数据
维普
