Construction of English Translation Model Based on Neural Network Fuzzy Semantic Optimal Control

Zhang, Bingjie; Liu, Yiming

doi:10.1155/2022/9308236

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3366802 [58], [61], [64], [68], [86], [72], [74], [76], [78], [81], [85], and [93] largely focus on various aspects of machine translation, including methodologies, approaches, and applications. In contrast, references [29], [38], [53], [45], [40], [41], [42], [47], [48], [35], [60], [61], [63], [64], [87], [69], [70], [71], [82], and [89] examine the use of deep learning, neural networks, and related technologies in machine translation and natural language processing.…”

Section: Discussionmentioning

confidence: 99%

“…The sources cited, namely references [18], [19], [27], [28], [51], [36], [37], [46], [47], [58], [64], [69], [86], [71], [75], [82], [83], and [91] discuss the topic of NLP and its use in translation and related domains. The use of fuzzy logic and feature extraction algorithms in translation and language analysis was discussed in [60], [61], [62], [63], [67], [68], [69], [77], [79], [82], and [90]. Regarding Human Evaluation and Cognitive factors, it has been observed that references [71], [76], [78], [80], [81], [88], and [92] analyzed the significance of human evaluation and cognitive factors in assessing the quality of translation.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al [68] provided a model for English translation to address the issue of neural networks that provide inconsistent results in English translation systems. The proposed model uses an external English data table to perform fuzzy semantic optimum control retrieval.…”

Section: Figure 4 Illustration Of Fuzzy Algorithm Operations In Trans...mentioning

confidence: 99%

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The Impact of Artificial Intelligence on Language Translation: A Review

Mohamed,

Khanan,

Bashir

et al. 2024

IEEE Access

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In the context of a more linked and globalized society, the significance of proficient crosscultural communication has been increasing to a position of utmost importance. Language functions as a crucial medium that establishes connections among people, corporations, and countries, thus demanding the implementation of precise and effective translation systems. This comprehensive review paper aims to contribute to the evolving landscape of AI-driven language translation by critically examining the existing literature, identifying key debates, and uncovering areas of innovation and limitations where the primary objective -is to provide a nuanced understanding of the current state of AI-driven language translation, along with emphasizing the advancements, challenges, and ethical considerations. In this review, ongoing debates surrounding AI-driven language translations were actively involved. By evaluating different viewpoints and methodologies, insights into unresolved questions that contribute to broader discourse in the field were provided. The future trajectory of this study is the incorporation of cross-lingual dialect adaptability and the advancement of Artificial Intelligence translation systems, with a focus on prioritizing inclusion and cultural understanding.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The Impact of Artificial Intelligence on Language Translation: A Review

Mohamed,

Khanan,

Bashir

et al. 2024

IEEE Access

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“…Using a fuzzy semantic optimal control retrieval approach to extract useful data from an English information table was proposed by the authors in [40] to improve the AES model's accuracy. This study uses a two-stage training method.…”

Section: Related Workmentioning

confidence: 99%

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

2023

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Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.

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“…Translation is currently the main tool for international communication between different countries [1][2][3][4][5][6][7][8]. In recent years, with the development of the language and translation service market, the demand for translation of academic papers and works with professional, practical and academic values has grown rapidly [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Translation Ability of Complex English Sentences Based on Artificial Neural Network

Xie¹,

Xiao²

2022

Int. J. Emerg. Technol. Learn.

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The current translation training programs at colleges and universities are not fully adequate for students majoring in English, and some students do not perform well in their execution of translation tasks. Translation ability is a frontier topic in the field of translation. A number of studies have provided some new ideas for research on the main goals of translation education and the evaluation of students’ translation abilities, but few has used artificial intelligence to predict students’ translation abilities. This paper analyzes the prediction of the translation ability with respect to complex English sentences. First, based on the principles of accuracy, operability and scientificity, an evaluation indicator system was built to fully reflect the basic characteristics of students’ ability to translate complex English sentences, which is in line with the training needs of translation talents at colleges and universities. Then, based on the characteristics of the logical structures of complex English sentences, a grey Bernoulli prediction model was constructed for the translation ability of complex English sentences. The particle swarm optimization algorithm was selected as the parameter optimization algorithm for the prediction model of translation ability with respect to complex English sentences, with the optimization targets being the Bernoulli parameters and the order of the fractional-order accumulation generation. The experimental results verified the effectiveness of the proposed model.

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Construction of English Translation Model Based on Neural Network Fuzzy Semantic Optimal Control

Cited by 7 publications

References 27 publications

The Impact of Artificial Intelligence on Language Translation: A Review

The Impact of Artificial Intelligence on Language Translation: A Review

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

Prediction of Translation Ability of Complex English Sentences Based on Artificial Neural Network

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