A text-based visual context modulation neural model for multimodal machine translation

Kwon, Soonmo; Go, Byung-Hyun; Lee, Jong-Hyeok

doi:10.1016/j.patrec.2020.06.010

Cited by 21 publications

(8 citation statements)

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“…e machine translation model overcomes two major problems: the low efficiency of serialized computation of traditional neural networks and the difficulty of modeling the long distance dependence of sentences in traditional neural networks, thus becoming the best Japanese translation model at present [12,13]. e process of this method is simple and intuitive.…”

Section: Machine Translation Model Analysismentioning

confidence: 99%

An Improved Machine Translation Model and its Application in Japanese Multi-Context Translation

Wen

2022

Security and Communication Networks

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In order to further improve the application of machine translation model in Japanese translation, analytic analysis method is adopted to optimize the original machine translation model. The improved machine translation model is used to analyze and describe Japanese translation. Finally, the optimized machine translation model is used to analyze Japanese multicontext. The relevant indexes and parameters were extracted and verified, and finally the model was verified by relevant experiments. The results show that the vector variation graph with different parameters can be divided into slow decline stage, stable change stage, and fast decline stage according to the increase of iteration number and the influence of corresponding change trend. In addition, it can be seen from the value of PE curve that the influence of parameter pe is the least, while the influence of corresponding re parameter is the greatest. The multicontext index of Japanese has the greatest influence on Japanese fluency and the least influence on Japanese keywords, and the trend of influence is parabolic. The application curve of the optimized machine translation model to Japanese in multiple contexts shows that different parameters have different effects on Japanese, which should be represented by the positive parameter V. Finally, the accuracy of the model is verified by experimental data. The above research can provide support for the application of machine learning in different fields and also provide research ideas for the multicontext translation of Japanese.

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Section: Machine Translation Model Analysismentioning

confidence: 99%

An Improved Machine Translation Model and its Application in Japanese Multi-Context Translation

Wen

2022

Security and Communication Networks

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“…e term "abnormal annual change" refers to financial information in the annual report that is inconsistent with the enterprise's operating process, resulting in anomalies. e term "industry horizontal anomaly" refers to when an enterprise's financial data considerably outperforms those of its competitors [11][12][13].…”

Section: Judgment Of Abnormal Financial Datamentioning

confidence: 99%

Financial Abnormal Data Detection System Based on Reinforcement Learning

Hou

2022

Mobile Information Systems

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The fraud of financial data has seriously damaged the interests of stakeholders and hindered the healthy development of the economy. Based on the reinforcement learning theory, this paper proposes a model of intelligent analysis of enterprise financial data anomalies, constructs the difference method, and transforms nontemporal indicators into temporal indicators. The proposed model uses a convolution neural network with four hidden layers to classify, evaluate, and analyze the constructed temporal indicators of financial data. The main objective is a more effective and accurate identification of the issue. The test results show that the intelligent analysis method of financial abnormal data based on deep learning has ideal effectiveness and accuracy.

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“…Related scholars provide a seed sentence segmentation method for the tree-based English machine translation system [25]. is method first divides the long sentence into shorter clauses, translates the clauses, and merges the subtranslations to generate the full sentence translation.…”

Section: Related Workmentioning

confidence: 99%

English Machine Translation Model Based on an Improved Self-Attention Technology

Pan

2021

Scientific Programming

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English machine translation is a natural language processing research direction that has important scientific research value and practical value in the current artificial intelligence boom. The variability of language, the limited ability to express semantic information, and the lack of parallel corpus resources all limit the usefulness and popularity of English machine translation in practical applications. The self-attention mechanism has received a lot of attention in English machine translation tasks because of its highly parallelizable computing ability, which reduces the model’s training time and allows it to capture the semantic relevance of all words in the context. The efficiency of the self-attention mechanism, however, differs from that of recurrent neural networks because it ignores the position and structure information between context words. The English machine translation model based on the self-attention mechanism uses sine and cosine position coding to represent the absolute position information of words in order to enable the model to use position information between words. This method, on the other hand, can reflect relative distance but does not provide directionality. As a result, a new model of English machine translation is proposed, which is based on the logarithmic position representation method and the self-attention mechanism. This model retains the distance and directional information between words, as well as the efficiency of the self-attention mechanism. Experiments show that the nonstrict phrase extraction method can effectively extract phrase translation pairs from the n-best word alignment results and that the extraction constraint strategy can improve translation quality even further. Nonstrict phrase extraction methods and n-best alignment results can significantly improve the quality of translation translations when compared to traditional phrase extraction methods based on single alignment.

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A text-based visual context modulation neural model for multimodal machine translation

Cited by 21 publications

References 1 publication

An Improved Machine Translation Model and its Application in Japanese Multi-Context Translation

An Improved Machine Translation Model and its Application in Japanese Multi-Context Translation

Financial Abnormal Data Detection System Based on Reinforcement Learning

English Machine Translation Model Based on an Improved Self-Attention Technology

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