The Ethical Choice of Business English Interpreters under Chesterman’s Model of Translation Ethics

This paper explores the ethical challenges encountered by business English interpreters using Chesterman’s Model of Translation Ethics,set against the context of economic globalization and the“Belt and Road”initiative.With the increasing demand for interpreters,the paper delves into the ongoing discussion about the role of AI in translation and its limitations,especially concerning cultural subtleties and ethical issues.It highlights the importance of human interpreters’cross-cultural understanding and the ethical principles that inform their work,such as the Ethics of Representation,Service,Communication,Norm-based Ethics,and Commitment.Moreover,the paper examines how these ethical models are applied in practical business situations,including business banquets,business negotiations,business talks and business visits,etc.,and investigates the cultural misunderstandings that may occur during these interactions.The study concludes that although AI provides efficiency and cost savings,human interpreters are essential for their capacity to handle the intricacies of cross-cultural communication with cultural awareness and ethical discernment.

A Study of Simile Translation in Red Sorghum Based on Relevance-Adaptation Model

In Red Sorghum, Mo Yan used about 200 similes, and Howard Goldblatt did an excellent job in translating them intoEnglish. The researcher aims to study the translation of similes in Red Sorghum based on Relevance-Adaptation Model. During theprocess of simile translation, a translator should first make a relevance assumption based on context to find the author's intention;second he should make a relevance adaptation to render the translated text conform to the tradition of the target language;last, aproper choice of utterance style should be made. The findings are as follows: for C-E translation, apart from translating a simile intoa simile, a translator can also translate it into a metaphor, paraphrase it or even omit the translation of it. Based on Relevance-Ad-aptation Model, this paper enriches studies on simile translation as well as on Red Sorghum.

LKMT:Linguistics Knowledge-Driven Multi-Task Neural Machine Translation for Urdu and English

Thanks to the strong representation capability of pre-trained language models,supervised machine translation models have achieved outstanding performance.However,the performances of these models drop sharply when the scale of the parallel training corpus is limited.Considering the pre-trained language model has a strong ability for monolingual representation,it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models.To alleviate the dependence on the parallel corpus,we propose a Linguistics Knowledge-Driven MultiTask(LKMT)approach to inject part-of-speech and syntactic knowledge into pre-trained models,thus enhancing the machine translation performance.On the one hand,we integrate part-of-speech and dependency labels into the embedding layer and exploit large-scale monolingual corpus to update all parameters of pre-trained language models,thus ensuring the updated language model contains potential lexical and syntactic information.On the other hand,we leverage an extra self-attention layer to explicitly inject linguistic knowledge into the pre-trained language model-enhanced machine translation model.Experiments on the benchmark dataset show that our proposed LKMT approach improves the Urdu-English translation accuracy by 1.97 points and the English-Urdu translation accuracy by 2.42 points,highlighting the effectiveness of our LKMT framework.Detailed ablation experiments confirm the positive impact of part-of-speech and dependency parsing on machine translation.

Research on high-performance English translation based on topic model

Retelling extraction is an important branch of Natural Language Processing(NLP),and high-quality retelling resources are very helpful to improve the performance of machine translation.However,traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application.In order to solve this problem,we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation performance.In this method,Probabilistic Latent Semantic Analysis(PLSA)is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition.Then we design a decoder to simplify the decoding process.Experiments show that the proposed method can effectively improve the accuracy of translation.

Regenerative Medicine in Orthopaedics: Microsurgery Achievements for Translational Animal Model

Purpose: Despite many scientific advances, Regenerative Medicine is still in the preclinical stages in many areas. In this article, we intend to discuss the role of microsurgery in the bench-to-bedside transition of such primary findings. Method: By searching the papers related to the history of Regenerative Medicine (RM) and the news of Tissue Engineering (TE) in orthopedics in Pubmed, Scopus, and Google Scholar databases, we accessed a complete archive of various topics related to this field. Result: We first assessed the history and achievements of regenerative medicine, then we realized the importance of translational medical sciences and the role of animal models in this incipient phenomenon. Finally, after mastering the capabilities of microsurgery and the useful contribution of this technique to the advancement of clinical applications of regenerative medicine in various branches such as skin, skeletal system, nerves, and blood vessels, we decided to express the gist of our studies through this article. Conclusion: Considering the widespread use of small animals in regenerative medicine projects and the inevitable role of microsurgery in performing the best intervention on these animal models, the significant progress of regenerative medicine clinical application requires special attention to microsurgery in associated research.

The Jerome Model and the Horace Model in Translation Research

In the research of translation theory,many scholars propose and construct translation models,the purpose of which is to try to describe the translating process and find out the nature of translation.This thesis mainly introduces and analyses the characteristics of the Jerome Model and the Horace Model,and try to make a comparison between these two models for studying translations,in the hope of leading to deeper recognition and research of the translation process.

Analysis on Application of Pragmatic Translation Skill Structure Model in Translation Teaching

As the core content of pragmatic translation teaching research,pragmatic translation skill is closely correlated with translation technique and operation field.Viewed from the current English teaching situation in colleges and universities,translation skill is not cultivated as an independent language competence.Moreover,its research is insufficient.On account of this,the author first gives an introduction to pragmatic translation skill and pragmatic translation skill structure model in this paper.On this basis,the author also analyzes the application of pragmatic translation skill structure model in translation teaching.

A Theoretic Study on the Translation Model from Relevance Theory

Relevance Theory holds that the language communication of human beings is an ostensive-inferential process of searching for relevance.Translation as a communication across language and culture,involves a dual process of communication among the source language author,the translator and the target language readers,during which the translator infers the communicative intention of the source language author,then by assessing the cognitive environments of the target language readers,makes the optimally relevant translation so that the translation can be equivalent in relevant aspects.

Improving Language Translation Using the Hidden Markov Model

Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.

Cultural Model in Chinese-to-English Translation： A Case Study of Guizhou＇s Local Culture

Culture is an important component in translation. This paper reviews cultural translation researches and practices, proposes a cultural model for Chinese-to-English translation composed of inputs, filters and outputs which could be realized by a number of methods and techniques in the translating process, and finally applies the model to Chinese-to-English translation of Guizhou＇s local culture. It is argued that the cultural model is expected to draw international target text readers close to Chinese culture.

Unified deep learning model for predicting fundus fluorescein angiography image from fundus structure image

The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.

A General Introduction of the Main Similarities and Differences between the Three Translation Models

This study focuses on the three translation models,the Jerome model,the Horace model,the Schleiermacher model.The study intends to compare the three models,there are some similarities and differences between the three models.By analyzing two kinds of the translation strategies,literal translation and free translation,this study argues that different source texts need different translation strategies.Translators should know the proper translation strategies and rich knowledge.

Improving Phrase-Based Statistical Machine Translation Models by Incorporating Syntax-Based Language Models

This paper proposed a method to incorporate syntax-based language models in phrase-based statistical machine translation (SMT) systems. The syntax-based language model used in this paper is based on link grammar,which is a high lexical formalism. In order to apply language models based on link grammar in phrase-based models,the concept of linked phrases,an extension of the concept of traditional phrases in phrase-based models was brought out. Experiments were conducted and the results showed that the use of syntax-based language models could improve the performance of the phrase-based models greatly.

Novel translational rat models of dopamine transporter deficiency

Dopamine （DA） is one of the brain＇s fundamental neurotransmitters. Despite the fact that the dopaminergic synapses constitute less than 1% of all brain synapses, DA is implicated in a number of critical physiological functions and in the pathogenesis of important psychiatric diseases such as schizophrenia, attention-deficit/hyperactivity disorder （ADHD）, Parkinson＇s disease （PD） and others.

The Renditions Series and Modern Chinese Literature’s “Going-out”: Its Model and Enlightenment of Translation, Communication and Dissemination

In the process of Chinese Literature’s “Going-out” project, it istypical of the dilemma faced by the external translation and dissemination of modernChinese literature. The Renditions Series serves as a classical case of providing aneffective model of “literature’s overseas distribution”. The successful experience of itstranslating-cum-disseminating model can be concluded as follows: all the translatorsare from China and overseas with cross-cultural background; the selection oftranslation is stylistically far-ranging among the Mainland, Taiwan and Hong Kong,and feminine literature in modern China is highly valued; translating method aims atconvoying the original literary aesthetics, and disseminating channels are manifold;the foreign recipients are common and professional readers; the eminent effects canbe proved by its considerable sales, library collections and acceptance in the targetcountries and regions. From the successful model of translation of Renditions Series,five implications can be made out for the modern Chinese literature’s “Going-global”:domestic and overseas translators cooperate well with all-round cultural identities; theselection holds onto diversification and literary character; various spreading channelsshould be broadened; target readers should be accurately located and continuouslywidened; translating and disseminating effects can be focused on and analyzed, so asto adjust and improve the corresponding sections in the model.

Research on the Strategic Model for the Translation of Taoist Classics Based on Adaptation Theory

The core view of the adaptation theory is that language has a systematic propagation law,and language translation should be unfolded and extended according to the basic structure of the system.This paper has conducted an in-depth study on the translation of classics of the Taoist culture,with the purpose of improving the accuracy of the translation strategy.This paper also focuses on adaptation theory and taking the continuation and adaptation process of discourse as the core of the research,and conducts the construction analysis on the strategic model.The content of this paper is predicted to offer some simple translation reference ideas for professional translators who are dedicated to the direction of classics.

RM-structure alignment based statistical machine translation model

A novel model based on structure alignments is proposed for statistical machine translation in this paper. Meta-structure and sequence of meta-structure for a parse tree are defined. During the translation process, a parse tree is decomposed to deal with the structure divergence and the alignments can be constructed at different levels of recombination of meta-structure （RM）. This method can perform the structure mapping across the sub-tree structure between languages. As a result, we get not only the translation for the target language, but sequence of meta-stmctu .re of its parse tree at the same time. Experiments show that the model in the framework of log-linear model has better generative ability and significantly outperforms Pharaoh, a phrase-based system.

Terminology Translations of Austin’s Speech Act Tripartite Model in China

Judging from the references, Chinese scholars have dozens of terminology translations on the British analytic philosopher Austin’s locutionary act, illocutionary act and perlocutionary act. There is no unified translation for it, and scholars did not cause enough attention to this. Thus teachers use the terms in the classroom inconsistently, resulting in doctoral students, graduates and undergraduates writing dissertations in confusion and chaos. This paper reviews and trims 25 frequently -used terminology translations of Speech Act Tripartite Model since 1955. Furthermore, some suggestions on Chinese terminology translations of it are put forward as well.

＂Flipped Class Model＂ Applied to Translation Class

With the development of computer technology, the way of language teaching has experienced great changes. In the past the methods of language teaching mainly focus on the direct method, the grammar-translation, audio-lingual, the structure approach, communicative language teaching, community language learning, task-based language learning, etc. These methods start and develop with the need and application of some theories. Now at the present time the computer is widely used in every field and the Internet is compulsory of daily life. So the ways of teaching language is changing accordingly. This paper is to analyze the new way of language teaching, the flipped class model. The first part is the general introduction of the flipped class model, and the second part is the application of the flipped class model to translation course.