The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to...The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to analyze the methods of its English-to-Chinese subtitle translation by considering social,cultural,and historic backgrounds between China and America.After data collection and case analysis,the study found that:(1)Five major translation methods are adopted in the subtitle translation of The Good Wife.They are free translation,variation,literal translation,addition,and omission.Among them,free translation is the most frequently used,while omission is used least.(2)The subtitle translation of films and TV series is limited by time and space restrictions,social-cultural differences,and other factors.When translating,translators should try to use humorous words,euphemism,intonation,and other ways,and combine different methods such as literal translation,free translation,variation,addition,omission,and other methods to seek equivalence both in the meaning and function of subtitles under the guidance of Functional Equivalence Theory.展开更多
Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecul...Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecular mechanisms involved in these types of scars and developing effective treatments requires the use of controlled ex-perimental models,especially animals,to overcome the limitations of clinical studies.The aim of this sistematic review is to critically analyze the animal models used in the last five years(2020-2025)for the study of pathological scars,highlighting their advantages,limitations and applicability in the development of new therapeutic strat-egies.Murine,rabbit and porcine models,as well as alternative models,offer varied perspectives on the formation and treatment of HTS and KS,with an emphasis on histological and molecular correlations with human pathology.By synthesizing recent data,the paper highlights the essential role of preclinical research in optimizing an-tifibrotic treatments and in advancing the translation of data into the clinical sphere.Overall,animal models remain essential for bridging mechanistic insights with clinical translation,supporting the development of more effective and personalized anti-scar therapies.展开更多
The lysosomal enzyme β-glucocerebrosidase(GCase) belongs to the family of glycosidases and hydrolyses the glycosphingolipid glucosylceramide(GluCer) into glucose and ceramide. The enzyme is of central importance for ...The lysosomal enzyme β-glucocerebrosidase(GCase) belongs to the family of glycosidases and hydrolyses the glycosphingolipid glucosylceramide(GluCer) into glucose and ceramide. The enzyme is of central importance for two pathologies:(1) the lysosomal storage disorder Gaucher's disease(GD) and(2) the neurodegenerative disorder Parkinson's disease(PD).展开更多
“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism ...“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism toward intelligen t,data centric manufacturing[2].By integrating computation,automation,and biology,AI gives rise to self-evolving,adaptive systems that learn from data,predict complex behaviors,and autonomously optimize fabrication outcomes.Such systems translate experimental insights into patient-specific and clinically relevant solutions,bridging laboratory research and regenerative therapies[3].This emerging frontier is rapidly advancing from concept to application.This Special Column highlights how AI-driven advanc es in materials,design,and manufacturing are reshaping biof abrication for regenerative medicine and clinical translation.展开更多
In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statist...In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters(DSAEF_LTD model).Three TC translation speed indicators most relevant to TC precipitation were incorporated:the maximum speed on Day 1(the first day of TC-induced precipitation and wind occurring on land)and the average and minimum speeds over All Days(all days of TC-induced precipitation and wind occurring on land),all classified using the Kmeans clustering algorithm.Simulation experiments showed that integrating TC translation speed enhanced the model's performance.The model provided a better optimal common scheme,with the TSS UM(sum of threat scores for severe and above and extremely severe and above disasters)increasing by 2.66%(from 0.5117 to 0.5253)compared with the original model.More importantly,its preassessment ability improved significantly,with the average TSS UM for independent samples increasing by 6.43%(from 0.6488 to0.6905).The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks.This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC.The enhanced preassessment ability can be attributed to the key role of TC translation speed,which significantly influences TC precipitation patterns and improves TC precipitation forecasting.Since precipitation is one of the most crucial disaster-causing factors,better TC precipitation forecasting leads to improved disaster preassessment outcomes.These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management,contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.展开更多
The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by the...The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by their interdisciplinary nature,regulatory constraints,and function-oriented definitions.Based on real-world translation examples,this paper analyzes the challenges in translating cosmetics ingredient terminology for international trade and proposes targeted translation strategies.The study aims to facilitate the global marketing of cosmetic products while ensuring consumers can accurately understand product ingredient information.展开更多
In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper propo...In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper proposes a Transformer-based multi-domain-aware mixture of experts model.To address the problem of domain feature differentiation,a mixture of experts(MoE)is introduced into attention to enhance the domain perception ability of the model,thereby improving the domain feature differentiation.To address the trade-off between domain feature distinction and cross-domain parameter sharing,we propose a domain-aware mixture of experts(DMoE).A domain-aware gating mechanism is introduced within the MoE module,simultaneously activating all domain experts to effectively blend domain feature distinction and cross-domain parameter sharing.A loss balancing function is then added to dynamically adjust the impact of the loss function on the expert distribution,enabling fine-tuning of the expert activation distribution to achieve a balance between domains.Experimental results on multiple Chinese-to-English and English-to-French datasets demonstrate that our proposed method significantly outperforms baseline models in both BLEU,chrF,and COMET metrics,validating its effectiveness in multi-domain neural machine translation.Further analysis of the probability distribution of expert activations shows that our method achieves remarkable results in both domain differentiation and cross-domain parameter sharing.展开更多
When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based ...When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based models offer strong baselines,they compromise syntactic awareness and the detection and man-agement of offensive content in cluttered,noisy,and informal text.In this paper,we present POSDEP-Offense-Trans,a multi-task NMT framework that combines Part-of-Speech(POS)and Dependency Parsing(DEP)methods with a robust offensive language classification module.Our architecture enriches the Transformer encoder with syntax-aware embeddings and provides syntax-guided attention mechanisms.The architecture incorporates a structure-aware contrastive loss that reinforces syntactic consistency and deploys auxiliary classification heads for POS tagging,dependency parsing,and multi-class offensive detection.The classifier for offensive words operates at both sentence and token levels and obtains guidance from syntactic features and formal finite automata rules that model offensive language structures-hate speech,profanity,sarcasm,and threats.Using this architecture,we construct a syntactically enriched,socially annotated corpus.Experimental results show improvements in translation quality,with a BLEU score of 33.5,UAS/LAS parsing accuracies of 92.4%and 90%,and a 4.5%Fl-score gain in offensive content detection compared with baseline POS+DEP+Offense models.Also,the proposed model achieved 92.3%in offensive content neutralization,as confirmed by ablation studies.This comprehensive English-Tamil NMT model that unifies syntactic modelling and ethical filtering-laying the groundwork for applications in social media moderation,hate speech mitigation,and policy-compliant multilingual content generation.展开更多
This paper examines the translation of imagery metaphors in Qu Yuan’s Li Sao using an Embodied-Cognitive Translatology(ECT)approach.It investigates how the poem’s intricate metaphors,such as orchid(lan),pepper and c...This paper examines the translation of imagery metaphors in Qu Yuan’s Li Sao using an Embodied-Cognitive Translatology(ECT)approach.It investigates how the poem’s intricate metaphors,such as orchid(lan),pepper and cassia(jiao and jun’gui),and crescent eyebrows(emei),are translated into English while maintaining their cultural,emotional,and philosophical depth.The study explores the challenges translators face when rendering these culturally specific metaphors,and it identifies strategies such as literal translation,adaptation,substitution,and paraphrasing used by translators to preserve the metaphors’essence.Drawing on the principles of ECT,the paper examines how metaphors function not just as linguistic elements but as cognitive symbols embedded in cultural contexts,reflecting the translator’s cognitive processes.The research highlights the importance of aligning the translation process with the cognitive and emotional resonance of the original text,thus ensuring the metaphor’s full meaning is conveyed.This paper also discusses the broader implications of translating metaphors from ancient Chinese literature,offering insights into the complexities of cross-cultural translation.It concludes with suggestions for future research in translating classical Chinese works,focusing on improving the preservation of metaphorical and cultural integrity.展开更多
To promote the global dissemination of Chinese culture,the translation of Chinese classic literature has garnered widespread attention in the translation field.Literary translation criticism plays an essential role in...To promote the global dissemination of Chinese culture,the translation of Chinese classic literature has garnered widespread attention in the translation field.Literary translation criticism plays an essential role in the development of translation endeavors.This paper will analyze three versions of English translations of Shen Congwen’s novella Border Town using Reiss’s translation criticism model and summarize their performance in terms of formal equivalence and aesthetic effect,linguistic components,and extra-linguistic components.展开更多
By comparing the two English versions of Hongloumeng, this paper discusses the reasons why the translators choose different translation strategies in dealing with the cultural context, and points out that translation ...By comparing the two English versions of Hongloumeng, this paper discusses the reasons why the translators choose different translation strategies in dealing with the cultural context, and points out that translation strategy is the translator＇s choice in different translational situations.展开更多
This paper takes Chinese red culture resources as its research subject and focuses on evaluating the Chinese-English translation quality of three major AI platforms:ChatGPT-4.0,ERNIE Bot,and DeepSeek.Through automatic...This paper takes Chinese red culture resources as its research subject and focuses on evaluating the Chinese-English translation quality of three major AI platforms:ChatGPT-4.0,ERNIE Bot,and DeepSeek.Through automatic quantitative evaluation,it systematically analyzes their performance in translating red culture texts.The study selects a diverse range of corpora,including historical documents,red classic texts,and culturally loaded terms.Three automatic evaluation metrics—GLEU,METEOR,and COMET—are employed for a comprehensive assessment.展开更多
This study investigates how artificial intelligence(AI)reshapes China’s translation industry through technological innovation,industrial restructuring,and talent demand evolution between 2015 and 2025.Analyzing empir...This study investigates how artificial intelligence(AI)reshapes China’s translation industry through technological innovation,industrial restructuring,and talent demand evolution between 2015 and 2025.Analyzing empirical data from 500 translation enterprises(including 320 small and medium-sized enterprises[SMEs]and 180 large-scale corporations),20 universities offering translation programs,and 12 key policy documents from China’s Ministry of Education(MOE),Cyberspace Administration of China(CAC),and Translators Association of China(TAC),we identify three distinct evolutionary phases of AI translation in China:rule-based systems(2010-2017),neural machine translation(NMT,2018-2022),and large language model(LLM)-driven intelligent adaptation(2023-2025).Key findings reveal that AI boosts translation efficiency by 40-60%through human-machine collaboration-reducing technical document processing time from 72 hours to 18 hours for complex BRI infrastructure blueprints-and expands service coverage to 34 languages(including 12 non-common languages critical for BRI,such as Kazakh,Swahili,and Urdu).However,AI also exacerbates talent mismatch:by 2025,China faced a shortage of over 50,000“AI-translation compound professionals”(individuals proficient in both translation and AI tool operation, ;corpus management,or LLM fine-tuning),particularly in high-stakes fields like legal contract translation and medical document localization.Ethical risks further challenge sustainability:a cross-sectional survey of 500 enterprises conducted in 2024-2025 found that 45%of SMEs violated data privacy regulations(e.g.,using client confidential documents to train LLMs without consent),and 30%of LLM-generated literary translations infringed copyrights.Policy recommendations include aligning with China’s New Generation AI Development Plan(2024-2030)to accelerate domain-specific standardization(e.g.,mandatory certification for legal and medical AI translation tools)and integrating AI literacy into translation curricula(e.g.,mandatory“LLM Operation and Quality Assessment”courses in all undergraduate translation programs by 2026).展开更多
The translation of ancient Chinese cultural artifacts terminology in museums plays a pivotal role in facilitating cross-cultural communication and preserving cultural heritage in the context of globalization.Through f...The translation of ancient Chinese cultural artifacts terminology in museums plays a pivotal role in facilitating cross-cultural communication and preserving cultural heritage in the context of globalization.Through field research in over ten Chinese museums and analysis of more than 800 terms,this paper explores the most effective translation methods for such terminology,focusing on transliteration and literal translation under the strategy of foreignization as well as liberal translation and imitation under the domesticating strategy.Standardized terminology not only prevents ambiguities but also bridges cultural gaps by preserving phonetic authenticity while contextualizing functionality.By refining translation practices,museums can better fulfill their mission as bridges between cultures,promoting global appreciation of China’s rich historical and artistic legacy.展开更多
文摘The Good Wife is an American TV series that focuses on women’s independence,politics,and law.The drama has been remade in China,Japan,and South Korea.This research aims to use Nida’s Functional Equivalence Theory to analyze the methods of its English-to-Chinese subtitle translation by considering social,cultural,and historic backgrounds between China and America.After data collection and case analysis,the study found that:(1)Five major translation methods are adopted in the subtitle translation of The Good Wife.They are free translation,variation,literal translation,addition,and omission.Among them,free translation is the most frequently used,while omission is used least.(2)The subtitle translation of films and TV series is limited by time and space restrictions,social-cultural differences,and other factors.When translating,translators should try to use humorous words,euphemism,intonation,and other ways,and combine different methods such as literal translation,free translation,variation,addition,omission,and other methods to seek equivalence both in the meaning and function of subtitles under the guidance of Functional Equivalence Theory.
基金Ministry of Research,Innovation and Digitization,CCCDI-UEFISCDI,Grant/Award Number:PN-IV-P7-7.1-PED-2024-1578,within PNCDI Ⅳ.
文摘Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecular mechanisms involved in these types of scars and developing effective treatments requires the use of controlled ex-perimental models,especially animals,to overcome the limitations of clinical studies.The aim of this sistematic review is to critically analyze the animal models used in the last five years(2020-2025)for the study of pathological scars,highlighting their advantages,limitations and applicability in the development of new therapeutic strat-egies.Murine,rabbit and porcine models,as well as alternative models,offer varied perspectives on the formation and treatment of HTS and KS,with an emphasis on histological and molecular correlations with human pathology.By synthesizing recent data,the paper highlights the essential role of preclinical research in optimizing an-tifibrotic treatments and in advancing the translation of data into the clinical sphere.Overall,animal models remain essential for bridging mechanistic insights with clinical translation,supporting the development of more effective and personalized anti-scar therapies.
基金supported by the Michael J Fox Foundation (to PA and FZ)。
文摘The lysosomal enzyme β-glucocerebrosidase(GCase) belongs to the family of glycosidases and hydrolyses the glycosphingolipid glucosylceramide(GluCer) into glucose and ceramide. The enzyme is of central importance for two pathologies:(1) the lysosomal storage disorder Gaucher's disease(GD) and(2) the neurodegenerative disorder Parkinson's disease(PD).
文摘“By successfully integrating artificial intelligence(AI)into research workflows,researchers could substantially increase scientific productivity”[1].In biofabrication,AI is dr iving a paradigm shift from empiricism toward intelligen t,data centric manufacturing[2].By integrating computation,automation,and biology,AI gives rise to self-evolving,adaptive systems that learn from data,predict complex behaviors,and autonomously optimize fabrication outcomes.Such systems translate experimental insights into patient-specific and clinically relevant solutions,bridging laboratory research and regenerative therapies[3].This emerging frontier is rapidly advancing from concept to application.This Special Column highlights how AI-driven advanc es in materials,design,and manufacturing are reshaping biof abrication for regenerative medicine and clinical translation.
基金supported by the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202307)the Basic Research Fund of CAMS(No.2023Z016)+1 种基金the National Natural Scientific Foundation of China(No.42275037)the Jiangsu Collaborative Innovation Center for Climate Change。
文摘In this study,tropical cyclone(TC)translation speed was introduced as a new similarity factor within the generalized initial value(GIV)framework,enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters(DSAEF_LTD model).Three TC translation speed indicators most relevant to TC precipitation were incorporated:the maximum speed on Day 1(the first day of TC-induced precipitation and wind occurring on land)and the average and minimum speeds over All Days(all days of TC-induced precipitation and wind occurring on land),all classified using the Kmeans clustering algorithm.Simulation experiments showed that integrating TC translation speed enhanced the model's performance.The model provided a better optimal common scheme,with the TSS UM(sum of threat scores for severe and above and extremely severe and above disasters)increasing by 2.66%(from 0.5117 to 0.5253)compared with the original model.More importantly,its preassessment ability improved significantly,with the average TSS UM for independent samples increasing by 6.43%(from 0.6488 to0.6905).The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks.This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC.The enhanced preassessment ability can be attributed to the key role of TC translation speed,which significantly influences TC precipitation patterns and improves TC precipitation forecasting.Since precipitation is one of the most crucial disaster-causing factors,better TC precipitation forecasting leads to improved disaster preassessment outcomes.These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management,contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.
基金funded by 2025 Graduate Teaching Construction Project of USST.
文摘The cosmetics industry operates on a global scale,making the accurate translation of ingredient terminology crucial for international trade and consumer comprehension.Cosmetic ingredient terms are characterized by their interdisciplinary nature,regulatory constraints,and function-oriented definitions.Based on real-world translation examples,this paper analyzes the challenges in translating cosmetics ingredient terminology for international trade and proposes targeted translation strategies.The study aims to facilitate the global marketing of cosmetic products while ensuring consumers can accurately understand product ingredient information.
基金supported by the NationalNatural Science Foundation of China(U2004163)Key Research and Development Program of Henan Province(No.251111211200).
文摘In multi-domain neural machine translation tasks,the disparity in data distribution between domains poses significant challenges in distinguishing domain features and sharing parameters across domains.This paper proposes a Transformer-based multi-domain-aware mixture of experts model.To address the problem of domain feature differentiation,a mixture of experts(MoE)is introduced into attention to enhance the domain perception ability of the model,thereby improving the domain feature differentiation.To address the trade-off between domain feature distinction and cross-domain parameter sharing,we propose a domain-aware mixture of experts(DMoE).A domain-aware gating mechanism is introduced within the MoE module,simultaneously activating all domain experts to effectively blend domain feature distinction and cross-domain parameter sharing.A loss balancing function is then added to dynamically adjust the impact of the loss function on the expert distribution,enabling fine-tuning of the expert activation distribution to achieve a balance between domains.Experimental results on multiple Chinese-to-English and English-to-French datasets demonstrate that our proposed method significantly outperforms baseline models in both BLEU,chrF,and COMET metrics,validating its effectiveness in multi-domain neural machine translation.Further analysis of the probability distribution of expert activations shows that our method achieves remarkable results in both domain differentiation and cross-domain parameter sharing.
文摘When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based models offer strong baselines,they compromise syntactic awareness and the detection and man-agement of offensive content in cluttered,noisy,and informal text.In this paper,we present POSDEP-Offense-Trans,a multi-task NMT framework that combines Part-of-Speech(POS)and Dependency Parsing(DEP)methods with a robust offensive language classification module.Our architecture enriches the Transformer encoder with syntax-aware embeddings and provides syntax-guided attention mechanisms.The architecture incorporates a structure-aware contrastive loss that reinforces syntactic consistency and deploys auxiliary classification heads for POS tagging,dependency parsing,and multi-class offensive detection.The classifier for offensive words operates at both sentence and token levels and obtains guidance from syntactic features and formal finite automata rules that model offensive language structures-hate speech,profanity,sarcasm,and threats.Using this architecture,we construct a syntactically enriched,socially annotated corpus.Experimental results show improvements in translation quality,with a BLEU score of 33.5,UAS/LAS parsing accuracies of 92.4%and 90%,and a 4.5%Fl-score gain in offensive content detection compared with baseline POS+DEP+Offense models.Also,the proposed model achieved 92.3%in offensive content neutralization,as confirmed by ablation studies.This comprehensive English-Tamil NMT model that unifies syntactic modelling and ethical filtering-laying the groundwork for applications in social media moderation,hate speech mitigation,and policy-compliant multilingual content generation.
文摘This paper examines the translation of imagery metaphors in Qu Yuan’s Li Sao using an Embodied-Cognitive Translatology(ECT)approach.It investigates how the poem’s intricate metaphors,such as orchid(lan),pepper and cassia(jiao and jun’gui),and crescent eyebrows(emei),are translated into English while maintaining their cultural,emotional,and philosophical depth.The study explores the challenges translators face when rendering these culturally specific metaphors,and it identifies strategies such as literal translation,adaptation,substitution,and paraphrasing used by translators to preserve the metaphors’essence.Drawing on the principles of ECT,the paper examines how metaphors function not just as linguistic elements but as cognitive symbols embedded in cultural contexts,reflecting the translator’s cognitive processes.The research highlights the importance of aligning the translation process with the cognitive and emotional resonance of the original text,thus ensuring the metaphor’s full meaning is conveyed.This paper also discusses the broader implications of translating metaphors from ancient Chinese literature,offering insights into the complexities of cross-cultural translation.It concludes with suggestions for future research in translating classical Chinese works,focusing on improving the preservation of metaphorical and cultural integrity.
文摘To promote the global dissemination of Chinese culture,the translation of Chinese classic literature has garnered widespread attention in the translation field.Literary translation criticism plays an essential role in the development of translation endeavors.This paper will analyze three versions of English translations of Shen Congwen’s novella Border Town using Reiss’s translation criticism model and summarize their performance in terms of formal equivalence and aesthetic effect,linguistic components,and extra-linguistic components.
文摘By comparing the two English versions of Hongloumeng, this paper discusses the reasons why the translators choose different translation strategies in dealing with the cultural context, and points out that translation strategy is the translator＇s choice in different translational situations.
基金Shanxi Normal University Graduate Innovation Project(2024XSY31)。
文摘This paper takes Chinese red culture resources as its research subject and focuses on evaluating the Chinese-English translation quality of three major AI platforms:ChatGPT-4.0,ERNIE Bot,and DeepSeek.Through automatic quantitative evaluation,it systematically analyzes their performance in translating red culture texts.The study selects a diverse range of corpora,including historical documents,red classic texts,and culturally loaded terms.Three automatic evaluation metrics—GLEU,METEOR,and COMET—are employed for a comprehensive assessment.
文摘This study investigates how artificial intelligence(AI)reshapes China’s translation industry through technological innovation,industrial restructuring,and talent demand evolution between 2015 and 2025.Analyzing empirical data from 500 translation enterprises(including 320 small and medium-sized enterprises[SMEs]and 180 large-scale corporations),20 universities offering translation programs,and 12 key policy documents from China’s Ministry of Education(MOE),Cyberspace Administration of China(CAC),and Translators Association of China(TAC),we identify three distinct evolutionary phases of AI translation in China:rule-based systems(2010-2017),neural machine translation(NMT,2018-2022),and large language model(LLM)-driven intelligent adaptation(2023-2025).Key findings reveal that AI boosts translation efficiency by 40-60%through human-machine collaboration-reducing technical document processing time from 72 hours to 18 hours for complex BRI infrastructure blueprints-and expands service coverage to 34 languages(including 12 non-common languages critical for BRI,such as Kazakh,Swahili,and Urdu).However,AI also exacerbates talent mismatch:by 2025,China faced a shortage of over 50,000“AI-translation compound professionals”(individuals proficient in both translation and AI tool operation, ;corpus management,or LLM fine-tuning),particularly in high-stakes fields like legal contract translation and medical document localization.Ethical risks further challenge sustainability:a cross-sectional survey of 500 enterprises conducted in 2024-2025 found that 45%of SMEs violated data privacy regulations(e.g.,using client confidential documents to train LLMs without consent),and 30%of LLM-generated literary translations infringed copyrights.Policy recommendations include aligning with China’s New Generation AI Development Plan(2024-2030)to accelerate domain-specific standardization(e.g.,mandatory certification for legal and medical AI translation tools)and integrating AI literacy into translation curricula(e.g.,mandatory“LLM Operation and Quality Assessment”courses in all undergraduate translation programs by 2026).
文摘The translation of ancient Chinese cultural artifacts terminology in museums plays a pivotal role in facilitating cross-cultural communication and preserving cultural heritage in the context of globalization.Through field research in over ten Chinese museums and analysis of more than 800 terms,this paper explores the most effective translation methods for such terminology,focusing on transliteration and literal translation under the strategy of foreignization as well as liberal translation and imitation under the domesticating strategy.Standardized terminology not only prevents ambiguities but also bridges cultural gaps by preserving phonetic authenticity while contextualizing functionality.By refining translation practices,museums can better fulfill their mission as bridges between cultures,promoting global appreciation of China’s rich historical and artistic legacy.
