WCC-JC: A Web-Crawled Corpus for Japanese-Chinese Neural Machine Translation

Zhang, Jinyi; Tian, Ye; Mao, Jiannan; Han, Moonsik; Matsumoto, Tadahiro

doi:10.3390/app12126002

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…This traditional translation process was time-consuming [ 17 ]. Zhang et al used web crawlers to obtain relevant information required for the text in the translation process, professionally classified the translated text, and established a professional data corpus to facilitate the translation work [ 18 ]. Shreffler et al conducted an experimental design on the preparation of translation work and found that MT could perform pretranslation work [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Design and Proofreading of the English‐Chinese Computer‐Aided Translation System by the Neural Network

Liu

Zhang²

2023

Computational Intelligence and Neuroscience

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At present, complete machine translation (MT) cannot meet the needs of information communication and cultural exchange, and the speed of complete human translation is too slow. Therefore, if MT is used to assist in the process of English-Chinese translation, it can not only prove that machine learning (ML) can translate English to Chinese but also improve the translation efficiency and accuracy of translators through human-machine cooperation. The research on the mutual cooperation between ML and human translation has an important research significance for translation systems. An English-Chinese computer-aided translation (CAT) system is designed and proofread based on a neural network (NN) model. First, it gives a brief overview of CAT. Second, the related theory of the NN model is discussed. An English-Chinese CAT and proofreading system based on the recurrent neural network (RNN) is constructed. Finally, the translation accuracy and proofreading recognition rate of the translation files of 17 different projects under different models are studied and analyzed. The research results reveal that according to the different translation properties of different texts, the average accuracy rate of text translation under the RNN model is 93.96%, and the mean accuracy of text translation under the transformer model is 90.60%. The translation accuracy of the RNN model in the CAT system is 3.36% higher than that of the transformer model. The English-Chinese CAT system based on the RNN model has different proofreading results for sentence processing, sentence alignment, and inconsistency detection of translation files of different projects. Among them, the recognition rate for sentence alignment and the inconsistency detection of English-Chinese translation is high, and the expected effect is achieved. The design of the English-Chinese CAT and proofreading system based on the RNN can make the translation and proofreading be carried out simultaneously, which greatly improves the efficiency of translation work. Meanwhile, the above research methods can improve the problems encountered in the current English-Chinese translation, provide a path for the bilingual translation process, and have certain promotion prospects.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Design and Proofreading of the English‐Chinese Computer‐Aided Translation System by the Neural Network

Liu

Zhang²

2023

Computational Intelligence and Neuroscience

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“…It is no exaggeration to say that after entering the 21st century, almost everyone who lives in the information network era has to deal with machine translation directly or indirectly. No matter in science and technology, business, or politics, machine translation is undoubtedly a very important practical subject [3,4]. e traditional machine translation method uses pipeline successive operations to mark the part of speech and analyze the syntax of the original corpus, so as to obtain the syntax structure of English language, which leads to the iterative transmission of errors between translation tasks and the reduction of the accuracy of structured examples, resulting in the reduction of the accuracy of English language and literature translation [5].…”

Section: Introductionmentioning

confidence: 99%

Cross-Context Accurate English Translation Method Based on the Machine Learning Model

Zhang¹

2022

Mathematical Problems in Engineering

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The era of big data and cloud computing has come, communication between different languages is becoming more and more common, and the barriers between languages are becoming more and more prominent. As the most important means to overcome language barriers, machine translation will play an increasingly important role in modern society. The previous machine translation technology has more or less disadvantages. The accuracy of translation is too low, which is a huge bottleneck hindering the further development of machine translation technology. Therefore, based on this, we can consider modeling the cross-context accurate English translation model based on the machine translation model and rely on the working principle of machine learning. This experiment shows that the translation accuracy of our method reaches 94.2%, which is higher than 39.5% of the benchmark method. This shows that the method in this paper can reduce the influence of other factors, ensure the accuracy of cross-context English translation to a certain extent, and meet the performance improvement requirements of the English translation system.

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“…Liu et al [29] introduced DuRecDial 2.0, a bilingual parallel dialog dataset for English and Chinese, aimed at advancing monolingual, multilingual, and cross-lingual conversational recommendation systems, showcasing the benefits of incorporating additional English data for Chinese conversational recommendations. Furthermore, Zhang et al [30,31] made significant contributions by developing the WCC-JC Japanese-Chinese translation corpus and the manually aligned WCC-JC 2.0, a large-scale Japanese-Chinese parallel corpus, through web crawling, providing considerable support for Japanese-Chinese translation research.…”

Section: Corpus Constructionmentioning

confidence: 99%

WCC-EC 2.0: Enhancing Neural Machine Translation with a 1.6M+ Web-Crawled English-Chinese Parallel Corpus

Zhang,

Su,

Tian

et al. 2024

Electronics

Self Cite

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This research introduces WCC-EC 2.0 (Web-Crawled Corpus—English and Chinese), a comprehensive parallel corpus designed for enhancing Neural Machine Translation (NMT), featuring over 1.6 million English-Chinese sentence pairs meticulously gathered via web crawling. This corpus, extracted through an advanced web crawler, showcases the vast linguistic diversity and richness of English and Chinese, uniquely spanning the rarely covered news and music domains. Our methodical approach in web crawling and corpus assembly, coupled with rigorous experiments and manual evaluations, demonstrated its superiority by achieving high BLEU scores, marking significant strides in translation accuracy and model resilience. Its inclusion of these specific areas adds significant value, providing a unique dataset that enriches the scope for NMT research and development. With the rise of NMT technology, WCC-EC 2.0 emerges not only as an invaluable resource for researchers and developers, but also as a pivotal tool for improving translation accuracy, training more resilient models, and promoting interlingual communication.

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WCC-JC: A Web-Crawled Corpus for Japanese-Chinese Neural Machine Translation

Cited by 7 publications

References 19 publications

[Retracted] Design and Proofreading of the English‐Chinese Computer‐Aided Translation System by the Neural Network

[Retracted] Design and Proofreading of the English‐Chinese Computer‐Aided Translation System by the Neural Network

Cross-Context Accurate English Translation Method Based on the Machine Learning Model

WCC-EC 2.0: Enhancing Neural Machine Translation with a 1.6M+ Web-Crawled English-Chinese Parallel Corpus

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