English-Chinese Machine Translation Based on Transfer Learning and Chinese-English Corpus

Xu, Bo

doi:10.1155/2022/1563731

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…In the literature on teaching English to Chinese translation, a number of study projects were suggested; a few recent works are reviewed here, Xu [21] have suggested that a transfer learning-based English-Chinese machine translation study methodology. It begins by outlining the principles of transfer learning, neural machine translation and related fields of study.…”

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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“…Parallel corpora are usually used in corpus translation studies. Parallel corpora aid in comprehending and analyzing translated texts, particularly in machine translation models that use sufficient training data (Xu, 2022;Zhai et al, 2020). By comparing the grammatical parallels and contrasts in a corpus of six translated texts, the translators' styles can be discovered (Wang, 2023;Wang & Wang, 2022).…”

Section: Parallel Corpusmentioning

confidence: 99%

Corpus Study of the Adverb of Degree ‘Feichang’ in Chinese and English Parallel Corpus

Karjo,

Chandra

2024

Jour, Eng, Fore, Lang, Teach, Reser

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The use of a parallel corpus for Chinese and English adverbs is still rarely explored, especially when comparing the Chinese adverb feichang with its English equivalents. ‘Feichang’ literally means "very", but English has various other words to convey the same words. The aim of this study is to compare the grammatical structure and semantic meaning of the Chinese adverb ‘feichang’ with its English equivalents using Chinese-English parallel corpus. To obtain the data, we used 50 sentences that contain the word ‘feichang’ in Chinese and English from the PKU parallel corpus. All sentences are related to the computer field. These sentences were then analyzed using AntConc, a concordance tool, to examine the grammatical structure and semantic aspects of the phrases. The findings showed that in Chinese, the adverb ‘feichang’ occurs in adjective and verb phrases. Meanwhile, in the English corpus, these phrases are rendered differently in five types of phrases: adjective, noun, verb, prepositional, and adverbial. Moreover, the word ‘feichang’ itself is rendered in a different lexicon, even though the meaning is the same. The results implied that one language can offer more varieties in expressing similar ideas, and parallel corpus can be a good source for analyzing these differences.

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“…This technological innovation not only enhances accessibility and inclusivity in diverse linguistic settings but also promotes global collaboration and understanding. Moreover, the continuous refinement and improvement of such systems signify a promising trajectory towards more effective and nuanced bilingual communication in an increasingly interconnected world [5].…”

Section: Introductionmentioning

confidence: 99%

Realization of Chinese-English Bilingual Speech Dialogue System using Machine Translation Technology

Zhao

2024

jes

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The realization of a Chinese-English bilingual speech dialogue system through machine translation technology involves developing a sophisticated system capable of seamlessly translating spoken language between Chinese and English in real-time. This system employs cutting-edge machine learning algorithms, neural networks, and natural language processing techniques to accurately interpret and translate speech inputs from one language to another. By integrating advanced speech recognition and translation models, users can engage in fluid and natural conversations across language barriers, opening up new possibilities for cross-cultural communication and interaction. This paper introduces a Statistical Phase-based Bilingual Speech (SPBS) system designed to facilitate seamless language translation and dialogue between multiple languages, with a focus on Chinese and English. Leveraging advanced machine learning models and techniques, such as Recurrent Neural Networks (RNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) architecture, the SPBS system achieves high translation accuracy, computational efficiency, and fluency of translations. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB. Furthermore, the paper presents the machine learning settings and training progress of the SPBS system, demonstrating its effectiveness in accurately classifying and translating speech inputs across languages. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB.

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English-Chinese Machine Translation Based on Transfer Learning and Chinese-English Corpus

Cited by 6 publications

References 24 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

Corpus Study of the Adverb of Degree ‘Feichang’ in Chinese and English Parallel Corpus

Realization of Chinese-English Bilingual Speech Dialogue System using Machine Translation Technology

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