Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence - Shen, Guorong (Frontiers in Psychology, 2022)
In order to improve text reading ability, a human-computer interaction method based on AI human-computer interaction is proposed. Firstly, the design of AI human-computer interaction model is constructed, which includes the SQuAD data set and the designed baseline model. There are three components: The coding layer based on cyclic neural network (RNN encoder layer), which aims to encode the problem and text into a hidden state; The interaction layer is used to integrate problems and text representation; The output layer connects two independent soft Max layers after a fully connected layer, one is used to obtain the starting position of the answer in the text, and the other is used to obtain the ending position. In the interaction layer of the model, this paper uses hierarchical attention and aggregation mechanism to improve text coding. The traditional model interaction layer has a simple structure, which leads to weak relevance between text and problems, and poor understanding ability of the model. Finally, the self attention model is used to further enhance the feature representation of text. The experimental results show that the improved model in this paper is compared with the public AI human-computer interaction reading comprehension model. According to the data in the table, the accuracy of the model in this paper is better than that of the baseline model, in which the EM value is increased by 1.4% and the F1 value is increased by 2.7%. However, compared with the improvement point 2, the EM value and F1 value of the model have decreased by 0.7%. It shows that the output layer has a certain impact on the effect of the model, and the improvement and optimization of the output layer can also improve the performance of the model. It is proved that AI human-computer interaction can effectively improve text reading ability.