The analysis of learning investment effect for artificial intelligence English translation model based on deep neural network
Abstract With the rapid development of multimodal learning technologies, this work proposes a Future-Aware Multimodal Consistency Translation (FACT) model. This model incorporates future information guidance and multimodal consistency modeling to improve translation quality and enhance language lear...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11282-6 |
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| author | Yan Zhang Shuangshuang Lyu |
| author_facet | Yan Zhang Shuangshuang Lyu |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | Abstract With the rapid development of multimodal learning technologies, this work proposes a Future-Aware Multimodal Consistency Translation (FACT) model. This model incorporates future information guidance and multimodal consistency modeling to improve translation quality and enhance language learning efficiency. The model innovatively integrates target future contextual information with a multimodal consistency loss function, effectively capturing the interaction between text and visual information to optimize translation performance. Experimental results show that, in the English-German translation task, the FACT model outperforms the baseline model in both Bilingual Evaluation Understudy (BLEU) and Meteor scores. The model achieves BLEU scores of 41.3, 32.8, and 29.6, and Meteor scores of 58.1, 52.6, and 49.6 on the Multi30K tset16, tset17, and Microsoft Common Objects in Context datasets, respectively, demonstrating its remarkable performance advantages. Significance analysis also verifies this result. Ablation experiments indicate that the future context information supervision function and multimodal consistency loss function are crucial for the model’s performance. Further language learning experiments show that the FACT model significantly outperforms the Transformer model in multiple metrics, encompassing learning efficiency (83.2 words/hour) and translation quality (82.7 points), illustrating its potential in language learning applications. In short, the FACT model holds high application value in multimodal machine translation and language learning. This work provides new ideas and methods, and advances future multimodal translation technology research and applications. |
| format | Article |
| id | doaj-art-641efd7dfd2540d3b931c400d0ff686c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-641efd7dfd2540d3b931c400d0ff686c2025-08-20T04:01:52ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-11282-6The analysis of learning investment effect for artificial intelligence English translation model based on deep neural networkYan Zhang0Shuangshuang Lyu1School of International Education, Jilin Engineering Normal UniversityShenyang Institute of EngineeringAbstract With the rapid development of multimodal learning technologies, this work proposes a Future-Aware Multimodal Consistency Translation (FACT) model. This model incorporates future information guidance and multimodal consistency modeling to improve translation quality and enhance language learning efficiency. The model innovatively integrates target future contextual information with a multimodal consistency loss function, effectively capturing the interaction between text and visual information to optimize translation performance. Experimental results show that, in the English-German translation task, the FACT model outperforms the baseline model in both Bilingual Evaluation Understudy (BLEU) and Meteor scores. The model achieves BLEU scores of 41.3, 32.8, and 29.6, and Meteor scores of 58.1, 52.6, and 49.6 on the Multi30K tset16, tset17, and Microsoft Common Objects in Context datasets, respectively, demonstrating its remarkable performance advantages. Significance analysis also verifies this result. Ablation experiments indicate that the future context information supervision function and multimodal consistency loss function are crucial for the model’s performance. Further language learning experiments show that the FACT model significantly outperforms the Transformer model in multiple metrics, encompassing learning efficiency (83.2 words/hour) and translation quality (82.7 points), illustrating its potential in language learning applications. In short, the FACT model holds high application value in multimodal machine translation and language learning. This work provides new ideas and methods, and advances future multimodal translation technology research and applications.https://doi.org/10.1038/s41598-025-11282-6Multimodal translationFuture information guidanceNeural networksLearning investment effectVisual information |
| spellingShingle | Yan Zhang Shuangshuang Lyu The analysis of learning investment effect for artificial intelligence English translation model based on deep neural network Scientific Reports Multimodal translation Future information guidance Neural networks Learning investment effect Visual information |
| title | The analysis of learning investment effect for artificial intelligence English translation model based on deep neural network |
| title_full | The analysis of learning investment effect for artificial intelligence English translation model based on deep neural network |
| title_fullStr | The analysis of learning investment effect for artificial intelligence English translation model based on deep neural network |
| title_full_unstemmed | The analysis of learning investment effect for artificial intelligence English translation model based on deep neural network |
| title_short | The analysis of learning investment effect for artificial intelligence English translation model based on deep neural network |
| title_sort | analysis of learning investment effect for artificial intelligence english translation model based on deep neural network |
| topic | Multimodal translation Future information guidance Neural networks Learning investment effect Visual information |
| url | https://doi.org/10.1038/s41598-025-11282-6 |
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