<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, an improved semantic learning model under multimodal data fusion is proposed to model both image attention features and problem attention features using a network architecture of collaborative attention learning, which can effectively reduce irrelevant feature interference and extract more distinguishable features for image and problem representations. In addition, to address the high dimensionality as well as complex computational problems in the multimodal data fusion process, multimodal bilinear decomposition methods are utilized in order to achieve a more effective fusion of visual features in images and text features in questions to capture more complex interactions between multiple models. Compared with TF-IDF and TextRank, the accuracy rate of the model in this paper is 9.7% and 12.1% higher than them, respectively. The F=16.15, p&lt;.05, for the first group of scores and the second group of scores for international students in Chinese language classes playing listening materials in a combination of audio, video, and Chinese characters. The F=8.527, p&lt;.05, for the second group of scores and the third group of scores.</p>
</abstract>

In this paper, an improved semantic learning model under multimodal data fusion is proposed to model both image attention features and problem attention features using a network architecture of collaborative attention learning, which can effectively reduce irrelevant feature interference and extract more distinguishable features for image and problem representations. In addition, to address the high dimensionality as well as complex computational problems in the multimodal data fusion process, multimodal bilinear decomposition methods are utilized in order to achieve a more effective fusion of visual features in images and text features in questions to capture more complex interactions between multiple models. Compared with TF-IDF and TextRank, the accuracy rate of the model in this paper is 9.7% and 12.1% higher than them, respectively. The F=16.15, p<.05, for the first group of scores and the second group of scores for international students in Chinese language classes playing listening materials in a combination of audio, video, and Chinese characters. The F=8.527, p<.05, for the second group of scores and the third group of scores.

A Study on the Teaching Practice of Multimodal Data Analysis in International Chinese Language Education for International Students in China

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, an improved semantic learning model under multimodal data fusion is proposed to model both image attention features and problem attention...

A Study on the Teaching Practice of Multimodal Data Analysis in International Chinese Language Education for International Students in China

Online veröffentlicht: 17. Okt. 2023

Seitenbereich: -

Eingereicht: 12. Nov. 2022

Akzeptiert: 27. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00687

Schlüsselwörter
Multimodal data fusion, Attention mechanism, Semantic learning model, Text features, Chinese language education

© 2023 Song Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Study on the Teaching Practice of Multimodal Data Analysis in International Chinese Language Education for International Students in China

Online veröffentlicht: 17. Okt. 2023

Seitenbereich: -

Eingereicht: 12. Nov. 2022

Akzeptiert: 27. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00687

SchlüsselwörterMultimodal data fusion, Attention mechanism, Semantic learning model, Text features, Chinese language education

© 2023 Song Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Multimodal data fusion, Attention mechanism, Semantic learning model, Text features, Chinese language education