Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence
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) an...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.853066/full |
| _version_ | 1818319139921461248 |
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| author | Guorong Shen |
| author_facet | Guorong Shen |
| author_sort | Guorong Shen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T10:04:22Z |
| format | Article |
| id | doaj.art-3d215a57ac9b4799a6eee037bc1fe17b |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-13T10:04:22Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-3d215a57ac9b4799a6eee037bc1fe17b2022-12-21T23:51:34ZengFrontiers Media S.A.Frontiers in Psychology1664-10782022-03-011310.3389/fpsyg.2022.853066853066Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial IntelligenceGuorong ShenIn 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2022.853066/fullAI human-computer interactionreading comprehensionneural networkattention mechanismSQuAD dataset |
| spellingShingle | Guorong Shen Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence Frontiers in Psychology AI human-computer interaction reading comprehension neural network attention mechanism SQuAD dataset |
| title | Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence |
| title_full | Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence |
| title_fullStr | Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence |
| title_full_unstemmed | Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence |
| title_short | Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence |
| title_sort | strategies for improving text reading ability based on human computer interaction in artificial intelligence |
| topic | AI human-computer interaction reading comprehension neural network attention mechanism SQuAD dataset |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.853066/full |
| work_keys_str_mv | AT guorongshen strategiesforimprovingtextreadingabilitybasedonhumancomputerinteractioninartificialintelligence |