A Study on Chinese-English Machine Translation Based on Transfer Learning and Neural Networks

Li, Congli

doi:10.1155/2022/8282164

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Li [25] have suggested a unique machine translation model that integrates transfer learning with translation expertise, based on bilingual translation between Chinese and English. It is composed of a recurrent neural network-based translation quality evaluation model and a self-focused network-based model.…”

Section: Methodsmentioning

confidence: 99%

Corpus-based Research on English-Chinese Translation Teaching Combining Vocabulary Learning and Practice

Nan Zhang

2024

jes

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As the global economy is developing, real time translation among major influential languages such as English, Chinese has become a necessity. Translation from English to Chinese is difficult since Chinese has a different grammar and ambiguous word boundaries. Furthermore, there are issues with the current Chinese-English machine translation, including difficult-to-understand extended sentences and inaccurate word translation. To address this drawback, the methodology for more precisely categorizing vocabulary in both English and Chinese is described in this research. The data are collected from UM-Corpus news dataset. Then, using SEGBOT: Neural Text Segmentation method, English words are segmented from the sentences available on the dataset. Afterward, the data are fed to pre-processing. In pre-processing segment; words missing in translation from the news dataset are eliminated and enhances the input data utilizing Iterated Square-Root Cubature Kalman Filter. The outcome from the pre-processing data is transferred to the Pyramidal Convolution Shuffle Attention Neural Network (PCSANN). The word order, grammatical structure, consistent style and smooth flow for English to Chinese translation are successfully classified by using PCSANN. The Waterwheel Plant Algorithm (WWPA) is used to optimize the weight parameter of PCSANN. The proposed PCSANN-WWPA is applied in python working platform. Performance metrics, like accuracy, precision, F1-score, and recall are examined to compute proposed method. The gained results of the proposed PCSANN-WWPA method attains higher accuracy of 16.65%, 18.85%, and 17.89%, higher sensitivity of 16.34%, 12.23%, and 18.54% and higher precision of 14.89%, 16.89%, and 18.23%. The proposed ECTT-PCSANN-WWPA method is compared with the existing methods such as ECTT-RNN, ECTT-DNN, and ECTT-MLPNN models respectively.

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Section: Methodsmentioning

confidence: 99%

Corpus-based Research on English-Chinese Translation Teaching Combining Vocabulary Learning and Practice

Nan Zhang

2024

jes

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“…Machine translation continuously updates the processing paths and means for natural language. The introduction of artificial intelligence has induced machine translation into the era of neural network translation, greatly improving translation speed and efficiency [5]; Applying the computer-assisted translation system to E-C translation and C-E translation has advanced the overall quality to a passing level or above, and enables users to basically achieve the communicative purpose [6]; The construction of a bilingual parallel corpus has provided the best platform for language research and translation research [7]. By constructing and applying a corpus for ICH translation research, researchers can more deeply explore and recognize the laws and characteristics of ICH texts at the linguistic level, and hence summarize objective and universal principles and methods for ICH translation [2].…”

Section: Introductionmentioning

confidence: 99%

A Corpus and Computer-Assisted Translation-Based Study on English Translation of Intangible Cultural Heritage Terms

Lv¹,

Jiang

2023

Frontiers in Artificial Intelligence and Applications

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The translation of intangible cultural heritage (ICH) texts presents significant challenges due to their rich cultural connotations and unique regional characteristics. To address this, the author of this study utilized Corpus and Computer-assisted Translation (CCAT) to assist in the English translation of ICH during the creation of a parallel corpus. Three patterns of CCAT-based English translation of ICH were proposed: machine translation, human-machine interactive translation (combining human and machine translation), and human translation. The implementation of these patterns can improve the efficiency and quality of ICH translation, promote terminology standardization, and facilitate the exportation and transmission of ICH.

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“…English learning is a learning process integrating various psychological activities, such as observation, simulation, cognition, reflection, and memorization [1][2][3][4]. From learning words, memorizing words, understanding grammar, to situational application, fragmented and personalized learning models enable students to establish conditional reflection to English sentences and contexts [5][6][7][8][9][10]. Distance English education platforms like ABC360 and Rocky English provides a new learning model, which facilitates the autonomous English learning [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Big Data-Based Behavior Analysis of Autonomous English Learning in Distance Education

Han¹

2022

Int. J. Emerg. Technol. Learn.

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Distance English education platforms like ABC360 and Rocky English provides a new learning model, which facilitates the autonomous English learning. This study aims to clarify the relationship between learning process and learning efficiency of distance English education, and provide personalized services for distance English teaching. To this end, we carried out a big data analysis on the behavior of autonomous English learning in distance education, in a bid to reveal the learning law and behavior pattern of the learners. Section 2 provides the research strategy for the motivation and behavior of autonomous English learning in distance education, and establishes a research model for such motivation and behavior. Section 3 constructs a behavior analysis model for autonomous English learning in distance education, computes the correlation between the target autonomous learning resources (ALRs) and the series of clicked ALRs, and calculates the probability for autonomous English learners in distance education to click on new resources. The proposed network model can handle a massive amount of learning behavior data, i.e., boast a certain ability to process the big data. The three parts of the model were introduced in details. The effectiveness of the model was demonstrated through experiments.

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A Study on Chinese-English Machine Translation Based on Transfer Learning and Neural Networks

Cited by 5 publications

References 19 publications

Corpus-based Research on English-Chinese Translation Teaching Combining Vocabulary Learning and Practice

Corpus-based Research on English-Chinese Translation Teaching Combining Vocabulary Learning and Practice

A Corpus and Computer-Assisted Translation-Based Study on English Translation of Intangible Cultural Heritage Terms

Big Data-Based Behavior Analysis of Autonomous English Learning in Distance Education

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