Chinese-English Parallel Corpus Research Articles

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

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.

Building a Parallel Corpus for Chinese Folk Songs Translation Studies: A Case Study of Northern Shaanxi and Hua’er Folk Songs

Folk songs, collaboratively created by the public and transmitted orally, have gained widespread popularity. The translation of folk songs primarily centers on lyrics translation, a subset of literary translation. Recent advancements in corpus technology have highlighted the significance of corpus-based research approaches for the analysis of literary translation. The corpus method, now employed as a hybrid research approach, enables the generation of quantitative data for descriptive translation studies. Scholars are increasingly using parallel corpora containing both the source text (ST) and the target text (TT) to explore translation universals across diverse texts. Despite the growing body of literature on the translation of Chinese folk songs, most studies have involved straightforward analyses of a limited number of translated texts without the utilization of quantitative approaches. This article aims to bridge this gap by presenting a prospective study on the creation of the Chinese-English Parallel Corpus of Northern Shaanxi and Hua’er Folk Songs (CEPCNSHFS). The study covers essential aspects such as sampling, corpus structure, corpora selection, and corpora processing. Moreover, to assess the practical utility of the CEPCNSHFS, a pilot study was conducted. The primary contributions of this article reside in the potential of the CEPCNSHFS to support diverse research topics, including the exploration of translation language characteristics, styles, and methods employed in translating Northern Shaanxi and Hua’er folk songs, both of which hold significant positions within Chinese folk song traditions.

A Corpus-based Study on China's Image in Emotions of Daodejing's English Translations from the Perspective of Communication Studies

Grounded in the Chinese-English parallel corpus of Daodejing, this study delves into the intricate interplay between emotional expression, communication effects, and the nuanced portrayal of China, unveiling the subtle impacts of translation in presenting the Chinese image. Through the establishment and analysis of the corpus, the research provides insights into the evolution of emotional elements in multiple translation versions spanning from 1868 to 2009. Quantitative analysis utilizing Pearson correlation coefficients reveals weak correlations between emotional expression and communication effects, both outbound and inbound . Despite a lack of statistical significance, qualitative analysis sheds light on valuable insights. In outbound communication, the nuanced handling of emotional expression, a weaker emphasis on positive emotions, and a deliberate avoidance of excessive negative emotions signify efforts to challenge traditional perceptions and portray a more complex image. In inbound communication, a tendency toward neutral emotional expression aims to offer Western readers a genuine and comprehensive understanding of China. Motivational analysis highlights the influence of cultural differences, historical contexts, reader expectations, and global dynamics on the translator's choices in emotional expression. Cultural nuances play a crucial role in shaping the Chinese image, emphasizing the translator's importance in flexibly navigating cross-cultural communication. In the era of globalization, this study aims to underscore the growing awareness among translators of the necessity to present cultural commonalities in emotional expression, fostering a broader understanding and acceptance of Chinese philosophy and values on the global stage.

Translator Stylistics: A Corpus Analysis of Two English Translations of Traditional Chinese Medicine Classic Da Sheng Bian

Da Sheng Bian, a significant work on obstetrics and gynecology that emerged in the early Qing Dynasty, was initially published as “A Treatise on Midwifery” in 1842 by William Lockhart, a British missionary to China. In 1894, John G. Kerr, an American missionary, translated the text as “The Tat Shang Pin”. This paper conducts a comparative study of the two English translations using a self-constructed English-Chinese parallel corpus of Da Sheng Bian. The study explores the translation styles of the two translators by examining the token-types ratio and frequency at the lexical level, mean sentence length at the syntactic level, and the use of conjunctions at the discourse level. The observed differences in translation styles between the two translations are analyzed in relation to the translators’ backgrounds and translation strategies.

Translation Purposes Determine Everything? Appellation Translation in Northern Shaanxi Folk Songs Based on Corpus Method

This study employs the skopos theory to analyze the translation methods used for appellations in Northern Shaanxi folk songs, a prominent genre in Chinese folk songs. The skopos theory emphasizes the importance of understanding and respecting the purpose of a translation, as it ultimately shapes the translation process and outcome. The English translations by Wang Hongyin and Wang Zhanbin are used to create a bilingual Chinese-English parallel corpus using a corpus technique. Additionally, to help the corpus software retrieve important statistics, the appellations and translation methods are manually annotated with the pertinent software. This study identifies seven primary categories of appellations, including those related to love, characters, laborers, family, mythology, allusions, and Buddhist figures. It explores the translation methods applied to each category, revealing that Wang Hongyin and Wang Zhanbin creatively adapt these appellations to convey their essence and significance to English-speaking readers while adhering to the skopos theory’s principles. This article contributes valuable insights into the translation of Northern Shaanxi folk songs, transcending cultural boundaries and enhancing cross-cultural understanding.

Retracted: Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese-English Parallel Corpus Noise Processing.

[This retracts the article DOI: 10.1155/2021/7144635.].

English-Chinese Corpus Collection and Artificial Intelligence Translation Based on Dynamic Clustering Model

This paper studies the English-Chinese corpus collection system and artificial intelligence translation function based on the dynamic clustering model. This paper attempts to introduce the semantic analysis of English-Chinese parallel corpora into the semantic recognition system. By labeling different levels of the corpus, including grammatical labeling, shallow semantic labeling and deep semantic labeling, the corresponding English-Chinese translation teaching system is designed, and the teaching system is improved by using the comparatively systematic teaching effects in actual teaching. On the basis of self-built Chinese-English parallel corpus, this paper extracts the Chinese high-frequency verb "to improve" to discover and summarize its English translation rules in different texts.

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

This paper proposes an English-Chinese machine translation research method based on transfer learning. First, it expounds the theory of neural machine translation and transfer learning and related technologies. Neural machine translation is discussed, the advantages and disadvantages of various models are introduced, and the transformer neural machine translation model framework is selected. For low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, 30 million Chinese-English parallel corpora, 100,000 Chinese-English low-resource parallel corpora, and 100,000 Tibetan-Chinese parallel corpora were used to pretrain the transformer machine translation architecture. The decoders are all composed of 6 identical hidden layers, the initialization of the model parameters is done by the transformer uniform distribution, and the model training uses Adam as the optimizer. In the model transfer part, the parameters with the better effect of the pretrained model are transferred to the low-resource Chinese-English and Tibetan-Chinese machine translation model training, so as to achieve the purpose of knowledge transfer. The results show that the model transfer learning of low-resource Chinese-English parallel corpus improves the translation system's translation by 3.97 BLEU values compared with the translation system without transfer learning at 0.34 BLEU values. Model transfer learning on low-resource Tibetan-Chinese parallel corpus increases the BLEU value by 2.64 BLEU compared to the translation system without transfer learning. The neural machine translation system that uses BPE technology for preprocessing plus model transfer learning is compared to the translation system that only performs transfer learning and shows an improved 0.26 BLEU value.It is verified that the transfer learning method proposed in this paper has a certain improvement in the effect of low-resource Chinese-English and Tibetan-Chinese neural machine translation models.

Research on the Analysis of Correlation Factors of English Translation Ability Improvement Based on Deep Neural Network.

This paper adopts the algorithm of the deep neural network to conduct in-depth research and analysis on the factors associated with the improvement of English translation ability. This study focuses on text complexity, adding discourse complexity features in addition to focusing on lexical and syntactic dimensions, exploring the application of neural network algorithm in the construction of text complexity grading model based on feature optimization, and examining the performance and generalization ability of the model. The rationality of the grading of the material is verified. After determining the model input features and training corpus, different classification algorithms were used to build the models and compare their performance. Meanwhile, compared with the models constructed based on common traditional readability formulas and other single-dimensional features, the models constructed based on the feature set of this study have significant advantages, with 20 to 30 percentage points higher in each performance evaluation index. The pseudo-parallel corpus is constructed, back translation is performed after obtaining the pseudo-parallel corpus, and finally, the data migration effect is measured and recorded on the low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, and the cycle continues until the model performance is no longer improved. The low-resource neural machine translation model based on model migration learning improved 3.97 and 2.64 BLEU values in the low-resource English translation task, respectively, and reduced the training time; based on this, the data migration learning method further improved 2.26 and 2.52 BLEU values.

Application of the Neural Network Based on the Multilayer Perceptron Genetic Algorithm in Chinese-English Two-Way Translation

With the increasing application of the perceptron genetic algorithm neural network in Chinese-English two-way translation, there are many translation problems to be solved. In order to solve the translation problem of Chinese-English parallel corpus, the multilayer perceptron method, genetic word alignment model (GA), language model, and neural network method (including the translation model and bilingual pretraining model) are designed, which are combined into the ga-mlp-nn combination model to measure the parallelism of Chinese and English sentences from different emphases. The results show that the ga-mlp-nn model has good performance in filtering high-quality parallel corpus. The final experimental results show that compared with a single system, the improved multisystem fusion method based on weight multiplication has achieved better results in the test set. In the last five groups of evaluation results, the system submitted in this paper ranks second and first in multiple datasets, which has a certain reference value for the research of corpus filtering.

Construction of Chinese-English Parallel Corpus of Chinese Laws and Regulations and Term Extraction Assisted by CAD Virtual Reality Technology

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

On the application of native cultural resources in English education of universities in ethnic minority areas——Taking Enshi Tujia and Miao Autonomous Prefecture as an example

Chinese universities have long given priority to the input of Western culture in English education while the native culture, namely the Chinese culture, has been seriously ignored. As a matter of fact, the model of English education based on the target culture can hardly meet the needs of the current situation of using English. As an international language communication tool, English should be fully integrated with the local pragmatic environment and used to serve the local economy. The acquisition of native culture can not only reduce learners’ anxiety and improve their learning efficiency, but also facilitate learners’ better understanding of Chinese and western cultures and improve their intercultural communicative competence as well. The following strategies can be adopted to apply the local cultural resources of Enshi Tujia and Miao Autonomous Prefecture to English education in its ethnic colleges and universities: 1) to foster the educational concept of attaching equal importance to Western culture and native culture; 2) To take local culture education an important part of college English curriculum and instructional design; 3) to build a Chinese-English parallel Corpus of local culture resources of Enshi; 4) to establish a practice base of local culture education

Construction of Medical Academic English Translation Model Driven by Bilingual Corpus-Based Data

With the rapid development of information collection technology and natural language processing technology, the construction of English-Chinese bilingual parallel corpus has developed rapidly. The scale of corpus and related technology have a large research space; and how to obtain effective data and knowledge from massive resources, in order to better serve the basic and applied research, is becoming a trend. Based on the research of parallel bilingual corpora at home and abroad, this article extracts the text features of medical English and constructs English-Chinese bilingual corpora with different levels aligned. Based on the statistical analysis of the distribution characteristics of phrase structures and the acquisition of characteristic knowledge, an English-Chinese bilingual translation model is constructed based on the bilingual corpus, and then the phrase structure knowledge in English and Chinese sentences is extracted from the data-driven English-Chinese bilingual corpus through the model. The results show that the accuracy of the test set is 91.63% and the F value is 90.05% under the condition of keeping the recall rate stable. The accuracy of the text structure features has been significantly improved, and the expected effect has been achieved through the test.

Research on the Construction and Application of Chinese-English Parallel Corpus of Red Tourism: Taking Hebei Province as a Case

Red tourism is the activities which enable tourists to realize the importance of the revolutionary spirit and receive revolutionary traditional education by visiting revolutionary memorials. Red tourism attracts more and more tourists with its unique cultural connotation. Its English translation is the key to opening up overseas markets. Building a Chinese-English bilingual corpus of red tourism is of great benefit to English translation research. This article first summarizes the theoretical and practical significance of the construction of a Chinese-English bilingual corpus of red tourism; then discusses the urgency of constructing a bilingual corpus of red tourism from two perspectives: improving the quality of red tourism English translation and telling red stories of Hebei; on this basis, the steps of corpus construction are proposed; finally, the application of the corpus is explained from three levels: serving for the study of the theory and practice of Chinese-English translation, serving for the teaching of Chinese-English translation, and serving for the language and cultural comparative research.

Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese-English Parallel Corpus Noise Processing.

This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese-English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win-win of time and efficiency.

ParaMed: a parallel corpus for English\u2013Chinese translation in the biomedical domain

BackgroundBiomedical language translation requires multi-lingual fluency as well as relevant domain knowledge. Such requirements make it challenging to train qualified translators and costly to generate high-quality translations. Machine translation represents an effective alternative, but accurate machine translation requires large amounts of in-domain data. While such datasets are abundant in general domains, they are less accessible in the biomedical domain. Chinese and English are two of the most widely spoken languages, yet to our knowledge, a parallel corpus does not exist for this language pair in the biomedical domain.DescriptionWe developed an effective pipeline to acquire and process an English-Chinese parallel corpus from the New England Journal of Medicine (NEJM). This corpus consists of about 100,000 sentence pairs and 3,000,000 tokens on each side. We showed that training on out-of-domain data and fine-tuning with as few as 4000 NEJM sentence pairs improve translation quality by 25.3 (13.4) BLEU for enrightarrowzh (zhrightarrowen) directions. Translation quality continues to improve at a slower pace on larger in-domain data subsets, with a total increase of 33.0 (24.3) BLEU for enrightarrowzh (zhrightarrowen) directions on the full dataset.ConclusionsThe code and data are available at https://github.com/boxiangliu/ParaMed.

The Construction and Application of an English-Chinese Parallel Corpus of Hydrogen Energy

This paper describes the construction and application of the first English-Chinese Parallel Corpus of Hydrogen Energy (PCOHE) in China, including design framework, collecting and processing of corpus materials, and its applications.Thepaper adopts statistical analysis with the help of corpussoftware to portray the linguistic characteristics of the hydrogen energy texts and study the translation strategy of the translated texts, which is expected to improve the efficiency and accuracy of translation. Although PCOHE is intended for translation studies of hydrogen energy texts, it can also be applied to such areas as the learning of hydrogen energy and machine translation.

ECCParaCorp: a cross-lingual parallel corpus towards cancer education, dissemination and application

BackgroundThe increasing global cancer incidence corresponds to serious health impact in countries worldwide. Knowledge-powered health system in different languages would enhance clinicians’ healthcare practice, patients’ health management and public health literacy. High-quality corpus containing cancer information is the necessary foundation of cancer education. Massive non-structural information resources exist in clinical narratives, electronic health records (EHR) etc. They can only be used for training AI models after being transformed into structured corpus. However, the scarcity of multilingual cancer corpus limits the intelligent processing, such as machine translation in medical scenarios. Thus, we created the cancer specific cross-lingual corpus and open it to the public for academic use.MethodsAiming to build an English-Chinese cancer parallel corpus, we developed a workflow of seven steps including data retrieval, data parsing, data processing, corpus implementation, assessment verification, corpus release, and application. We applied the workflow to a cross-lingual, comprehensive and authoritative cancer information resource, PDQ (Physician Data Query). We constructed, validated and released the parallel corpus named as ECCParaCorp, made it openly accessible online.ResultsThe proposed English-Chinese Cancer Parallel Corpus (ECCParaCorp) consists of 6685 aligned text pairs in Xml, Excel, Csv format, containing 5190 sentence pairs, 1083 phrase pairs and 412 word pairs, which involved information of 6 cancers including breast cancer, liver cancer, lung cancer, esophageal cancer, colorectal cancer, and stomach cancer, and 3 cancer themes containing cancer prevention, screening, and treatment. All data in the parallel corpus are online, available for users to browse and download (http://www.phoc.org.cn/ECCParaCorp/).ConclusionsECCParaCorp is a parallel corpus focused on cancer in a cross-lingual form, which is openly accessible. It would make up the imbalance of scarce multilingual corpus resources, bridge the gap between human readable information and machine understanding data resources, and would contribute to intelligent technology application as a preparatory data foundation e.g. cancer-related machine translation, cancer system development towards medical education, and disease-oriented knowledge extraction.

Automatic translation of spoken English based on improved machine learning algorithm

Due to the complexity of English machine translation technology and its broad application prospects, many experts and scholars have invested more energy to analyze it. In view of the complex and changeable English forms, the large difference between Chinese and English word order, and insufficient Chinese-English parallel corpus resources, this paper uses deep learning to complete the conversion between Chinese and English. The research focus of this paper is how to use language pairs with rich parallel corpus resources to improve the performance of Chinese-English neural machine translation, that is, to use multi-task learning to train neural machine translation models. Moreover, this research proposes a low-resource neural machine translation method based on weight sharing, which uses the weight-sharing method to improve the performance of Chinese-English low-resource neural machine translation. In addition, this study designs a control experiment to analyze the effectiveness of this study model. The research results show that the model proposed in this paper has a certain effect.

Machine Translation and Parallel Corpora: The Case of English-to-Chinese Translation of Relative Clauses

This paper compares translations done by professional human translators (as manifested in the Babel English-Chinese Parallel Corpus) and machine translations produced by Google Translate (with English as the source language and Simplified Chinese as the target language). Capitalizing on the branching direction of these two languages, we investigated translation strategies of restrictive relative clauses by focusing on which-relatives. The specific objectives of the study were to (a) extract relevant data from a parallel corpus; (b) assess machine translation quality; and (c) highlight the similarities and dissimilarities between corpus outputs and machine translation outputs. Of the 147 test materials, 115 Google Translate outputs (78.2%) were rated as ‘successful’ or ‘acceptable’. No significant differences were found between the degree of success in machine translations and linguistic factors (e.g., the active or passive voice in relative clauses). This finding confirms that linguistic knowledge is not required when using statistical machine translation (SMT) such as Google Translate. Another noteworthy finding was that Google Translate did not use the least frequently occurring translation strategy in the parallel corpus. This is not surprising given that SMT systems greatly rely on parallel corpora for training statistical translation models.