The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on s...The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.展开更多
This study aims to construct an automotive English corpus to comprehensively compare the differences between English automotive marketing texts and their Chinese translations.The objective is to reveal challenges and ...This study aims to construct an automotive English corpus to comprehensively compare the differences between English automotive marketing texts and their Chinese translations.The objective is to reveal challenges and opportunities in cultural and contextual translation.The research holds significant importance for understanding the impact of cross-cultural communication in the automotive market and providing more effective translation strategies for multinational automotive manufacturers.Through corpus analysis,focusing on common marketing phrases and text features,employing both quantitative and qualitative analysis methods,and examining the accuracy,naturalness,and cultural adaptability of translated texts,we delve into the similarities and differences in conveying automotive information between the two languages.The study finds that expressive and emotional expressions commonly used in English automotive contexts may encounter challenges in Chinese translations due to language and cultural differences.This necessitates the adoption of more flexible translation strategies.Additionally,Chinese translations tend to emphasize the practicality and safety of products more than their English counterparts,placing a greater emphasis on technical and functional descriptions.The primary conclusion of this research is that the translation of automotive marketing texts requires heightened cross-cultural sensitivity and an understanding of the target audience.When translating automotive advertisements and promotions,translators should consider consumer expectations and cultural values in different contexts to ensure the effectiveness and adaptability of the translation.Furthermore,the formulation of more flexible translation strategies,integrating local culture and market demands,will contribute to enhancing the image and influence of automotive brands in the international market.Through this study,we provide deeper insights for automotive manufacturers,assisting them in leveraging the power of language for successful global market penetration.展开更多
For more than a thousand years since the formation of translation as a discipline,many translation theories and different translation schools have emerged.The exploration of translation theories has lasted for hundred...For more than a thousand years since the formation of translation as a discipline,many translation theories and different translation schools have emerged.The exploration of translation theories has lasted for hundreds and thousands of years.Each translation school has proposed different translation theories.Throughout the development of translation theory,discussions have always centered on the relationship between the source text and the target text,encompassing translation strategies such as foreignization and domestication,as well as methods like literal translation and free translation.This relationship has been the main thread running through the history of translation theory development.In the 1970s,Reiss,a representative of the German functionalist school,put forward the text type theory.She linked the function of language with language type and text to study translation.She explored the different functions of various types of source texts from the perspective of text type.Therefore,in this paper,the functions and positions of the source text in translation are the central issues.This paper consists of two parts.In the first part,the history of Western translation development is divided into three periods:classical,modern,and contemporary.It uses time as a clue to analyze the changes in the position and function of the source text in translation.In the second part,it uses Reiss’s text type theory as a basis to analyze the extent to which the source text restricts the translator.展开更多
English text sentiment orientation analysis is a fundamental problem in the field of natural language processing.The traditional word segmentation method can produce ambiguity when dealing with English text.Therefore,...English text sentiment orientation analysis is a fundamental problem in the field of natural language processing.The traditional word segmentation method can produce ambiguity when dealing with English text.Therefore,this paper proposes a novel English text sentiment analysis based on convolutional neural network and U-network.The proposed method uses a parallel convolution layer to learn the associations and combinations between word vectors.The results are then input into the hierarchical attention network whose basic unit is U-network to determine the affective tendency.The experimental results show that the accuracy of bias classification on the English review dataset reaches 93.45%.Compared with many existing sentiment analysis models,it has more accuracy.展开更多
文摘The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.
文摘This study aims to construct an automotive English corpus to comprehensively compare the differences between English automotive marketing texts and their Chinese translations.The objective is to reveal challenges and opportunities in cultural and contextual translation.The research holds significant importance for understanding the impact of cross-cultural communication in the automotive market and providing more effective translation strategies for multinational automotive manufacturers.Through corpus analysis,focusing on common marketing phrases and text features,employing both quantitative and qualitative analysis methods,and examining the accuracy,naturalness,and cultural adaptability of translated texts,we delve into the similarities and differences in conveying automotive information between the two languages.The study finds that expressive and emotional expressions commonly used in English automotive contexts may encounter challenges in Chinese translations due to language and cultural differences.This necessitates the adoption of more flexible translation strategies.Additionally,Chinese translations tend to emphasize the practicality and safety of products more than their English counterparts,placing a greater emphasis on technical and functional descriptions.The primary conclusion of this research is that the translation of automotive marketing texts requires heightened cross-cultural sensitivity and an understanding of the target audience.When translating automotive advertisements and promotions,translators should consider consumer expectations and cultural values in different contexts to ensure the effectiveness and adaptability of the translation.Furthermore,the formulation of more flexible translation strategies,integrating local culture and market demands,will contribute to enhancing the image and influence of automotive brands in the international market.Through this study,we provide deeper insights for automotive manufacturers,assisting them in leveraging the power of language for successful global market penetration.
文摘For more than a thousand years since the formation of translation as a discipline,many translation theories and different translation schools have emerged.The exploration of translation theories has lasted for hundreds and thousands of years.Each translation school has proposed different translation theories.Throughout the development of translation theory,discussions have always centered on the relationship between the source text and the target text,encompassing translation strategies such as foreignization and domestication,as well as methods like literal translation and free translation.This relationship has been the main thread running through the history of translation theory development.In the 1970s,Reiss,a representative of the German functionalist school,put forward the text type theory.She linked the function of language with language type and text to study translation.She explored the different functions of various types of source texts from the perspective of text type.Therefore,in this paper,the functions and positions of the source text in translation are the central issues.This paper consists of two parts.In the first part,the history of Western translation development is divided into three periods:classical,modern,and contemporary.It uses time as a clue to analyze the changes in the position and function of the source text in translation.In the second part,it uses Reiss’s text type theory as a basis to analyze the extent to which the source text restricts the translator.
文摘English text sentiment orientation analysis is a fundamental problem in the field of natural language processing.The traditional word segmentation method can produce ambiguity when dealing with English text.Therefore,this paper proposes a novel English text sentiment analysis based on convolutional neural network and U-network.The proposed method uses a parallel convolution layer to learn the associations and combinations between word vectors.The results are then input into the hierarchical attention network whose basic unit is U-network to determine the affective tendency.The experimental results show that the accuracy of bias classification on the English review dataset reaches 93.45%.Compared with many existing sentiment analysis models,it has more accuracy.
