Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence.

Guorong Shen

doi:10.3389/fpsyg.2022.853066

Abstract

In order to improve text reading ability, a human-computer interaction method based on artificial intelligence (AI) human-computer interaction is proposed. Firstly, the design of the AI human-computer interaction model is constructed, which includes the Stanford Question Answering Dataset (SQuAD) and the designed baseline model. There are three components: the coding layer is based on a cyclic neural network (recurrent 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 manuscript 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 manuscript 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 manuscript is better than that of the baseline model, in which the exact match (EM) value is increased by 1.4% and the F1 value is increased by 2.7%. However, compared with improvement point 2, the EM and F1 values 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.

Highlights

Artificial intelligence (AI) has originated around 1950 and has gone through more than half a century
The language model used in this manuscript is Bidirectional Encoder Representation from Transformers (BERT), which is better than Embeddings from Language Models (ELMo) and OpenAI human-computer interaction generative pre-training model (GPT)
It can be seen from the data in the table that the exact matching value of the cyclic neural network model added with BERT in the verification set has increased by 0.2%, and the F1 value has increased by 2.2%

Summary

INTRODUCTION

Artificial intelligence (AI) has originated around 1950 and has gone through more than half a century During this period, it experienced two declines and rose for the third time in recent years. China’s graded reading software has begun to develop Chinese graded reading evaluation standards. The school can obtain the reading data report of the whole school through the headmaster, track the dynamic development of the school or regional reading level, and master the reading status of different grades and classes in real time, to facilitate macro-control and data analysis (Comunian, 2015). An ancient educator, positioned teachers as "preaching, teaching and dispelling doubts." The application of graded reading software in primary school Chinese teaching can replace some functions of teachers’ "teaching and dispelling doubts," help to share some heavy and trivial work and reduce teachers’ teaching pressure. That teachers can concentrate on more valuable work, that is, preaching and cultivate people with ideas, knowledge, wisdom, temperature, and soul (Martínez-Álvarez et al, 2015)

LITERATURE REVIEW

Evaluation Index

RESULTS AND ANALYSIS

Method

CONCLUSION