基于上下文嵌入和叠加注意力的机器阅读理解OA

Machine reading comprehension,one of the research directions of natural language processing,aims to improve the ability of computers to read and understand text content.Because the previous classical models do not consider long-term con-text dependence and polysemy,a single attention mechanism cannot fully express the meaning of the text.According to the above problems,this paper proposes an algorithm model,which improves the accuracy of word embedding by understanding the context in the embedding layer on top of the previous classical model.The accurate understanding of polysemy has a certain improvement,and the correlation between the question to the text and the text to the question is enhanced by superposition calculation,and the text meaning of attention expression is improved.On SQuAD dataset,the experimental results show that the performance of the model is significantly improved compared with the baseline model.