Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval

Song, Gang

doi:10.3233/jifs-189211

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…Weakly parallel corpora do not have as strict alignment requirements as parallel corpora. The editor is relatively easy to edit and construct weakly parallel corpora and it introduces multidimensional methods to obtain English-Chinese parallel corpora to solve machine learning problems [ 29 , 30 ].…”

Section: Construction Of the Machine Translation Model Based On Deep ...mentioning

confidence: 99%

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Sun

2022

Computational Intelligence and Neuroscience

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With the advent of the information age, people can establish good communication through Internet technology. Mechanical translation has become a key means to solve people’s communication problems. However, there are still obstacles to communication between different languages. In order to solve this problem, this paper uses existing neural network technology to the English-Chinese bidirectional machine translation model in the field of marine science and technology. Based on deep learning technology, we collect Chinese and English abstracts and partial full texts of Chinese and English papers with marine science and technology as the key words and build a professional corpus in English and Chinese about marine science and technology. In the Chinese-English bidirectional translation model, the local weight sharing is introduced into the Chinese encoder and the English encoder, and the output of the Chinese encoder sublayer and the English encoder sublayer is fused as the output of the respective encoders, and the performance of the translation model is evaluated using the BLEU parameters. Through the training of the translation model, compared with the transformer model, the BLEU value of the model with local weight sharing and encoder sublayer fusion output is improved by 1.6 and 3.8 in the Chinese-English and English-Chinese translation directions, respectively. The PPL values in the Chinese-English and English-Chinese translation directions decreased by 18.72% and 14.62%, respectively. We demonstrate the effectiveness of the language translation model. Experiments show that the research on machine language adaptive technology based on deep learning can more smoothly realize the two-way translation of literature in the field of marine science and technology. Compared with traditional mechanical translation, this paper proposes a translation model based on the deep neural algorithm, which improves the effect of model training by constructing a Chinese-English corpus with the theme of marine science and technology.

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Section: Construction Of the Machine Translation Model Based On Deep ...mentioning

confidence: 99%

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Sun

2022

Computational Intelligence and Neuroscience

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“…This is caused by the deep learning neural network layer of the recognition machine. Introduce the residual network model, before the level output, through the use of identity mapping, let the output layer cross the previous layer for data entry, and execute the identity mapping signal to avoid the network layer being too deep [18].…”

Section: Recognize the Characteristic Contours Of Dynamicmentioning

confidence: 99%

Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning

2021

Wireless Communications and Mobile Computing

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The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the F value is 0.92, with good comprehensive performance.

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“…A recognition model for Japanese characters is constructed by Literature [16] through the use of machine learning techniques that incorporate image feature retrieval techniques. Then, through the bilateral grid enhancement function to make the extended image features more prominent, and the HRD image to transform pixels into data, and finally take simulation experiments to confirm its recognition and translation ability, which is better than the traditional model.…”

Section: Introductionmentioning

confidence: 99%

A Study of the Assistive Nature of Artificial Intelligence Technology for Japanese Translation and Interpretation

He,

Shi

2024

Applied Mathematics and Nonlinear Sciences

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Traditional Japanese translation methods have certain disadvantages, and the introduction of artificial intelligence technology into them can enhance the effect of Japanese interpretation and translation. In this paper, the Japanese language data of Twitter and Facebook are used as the basis to construct a Japanese language interpretation and translation corpus, and the GPT-2 model is constructed on the basis of Transformer for Japanese text translation. To optimize the Seq2Seq model for Japanese speech interpretation, the Attention mechanism is introduced to establish a Japanese speech translation model. A Japanese oral and written corpus was used to analyze the validity of the methods mentioned above. The results show that the class/form ratio in the Japanese oral/translated corpus fluctuates between [0.1231, 0.1448], but the survival rate of borrowed words under the scientific category reaches the highest of 54.14%, and the average number of occurrences of each word is between [4.35, 4.95]. Japanese verbal and translated texts had an average sentence length of 40 hours, and their translation accuracy was approximately 74.16%. The quality of translation can be effectively improved, and cultural exchange between China and Japan can be enhanced by integrating AI technology with Japanese interpretation and translation.

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Accuracy analysis of Japanese machine translation based on machine learning and image feature retrieval

Cited by 12 publications

References 16 publications

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Dynamic Gesture Contour Feature Extraction Method Using Residual Network Transfer Learning

A Study of the Assistive Nature of Artificial Intelligence Technology for Japanese Translation and Interpretation

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