Translation System Research Articles

Construction of English corpus oral instant translation model based on information security and deep learning

In order to improve the quality of translation, avoid translation ambiguity and accurately present the content of the source language, supported by the concept of deep learning and guaranteed by information security, an instant oral translation model is constructed for English corpus. The aim of this study is to enhance the efficiency and accuracy of oral translation systems through the application of deep learning algorithms. Specifically, we employ a sample training mechanism tailored to the unique characteristics of oral translation, allowing for separate training of system interaction and translation data. Furthermore, by redesigning the interaction hardware, this research comprehensively redefines the hardware structure of the translation system, marking a significant step towards improving the usability and performance of such systems. After obtaining and processing effective security sensitive information, language resources are managed by using database management system, which fundamentally improves the level of network information security. The performance of the existing oral automatic translation system (Test Group 1) and the system designed in this paper (Test Group 2) is tested by experiments, and the results are as follows: (1) The translation system designed here has better interactive performance, and it is better than Test Group 1. (2) The adaptive index value of Test Group 1 is 1, and that of Test Group 2 is 0.5, which proves that the adaptive ability of system algorithm of Test Group 2 is better than that of Test Group 1. (3) When comparing the translation speed, the translation time of Test Group 2 is only 70.7 s, while that of Test Group 1 is 130.6 s, so the proposed translation system is obviously superior to that of Test Group 1.

Harnessing ZIKV NS2A RNA for alleviating acute hepatitis and cytokine release storm by targeting translation machinery.

Hyperactivated inflammatory responses induced by cytokine release syndrome are the primary causes of tissue damage and even death. The translation process is precisely regulated to control the production of proinflammatory cytokines. However, it is largely unknown whether targeting translation can effectively limit the hyperactivated inflammatory responses during acute hepatitis and graft-versus-host disease. By using in vitro translation and cellular overexpression systems, we have found that the nonstructural protein gene NS2A of Zika virus functions as RNA molecules to suppress the translation of both ectopic genes and endogenous proinflammatory cytokines. Mechanistically, results from RNA pulldown and co-immunoprecipitation assays have demonstrated that NS2A RNA interacts with the translation initiation factor eIF2α to disrupt the dynamic balance of the eIF2/eIF2B complex and translation initiation, which is the rate-limiting step of translation. In the acetaminophen-induced, lipopolysaccharide/D-galactosamine-induced, viral infection-induced acute hepatitis, and graft-versus-host disease mouse models, mice with myeloid cell-specific knock-in of NS2A show decreased levels of serum proinflammatory cytokines and reduced tissue damage. Zika virus NS2A dampens the production of proinflammatory cytokines and alleviates inflammatory injuries by interfering translation process as RNA molecules, which suggests that NS2A RNA is potentially used to treat numerous acute inflammatory diseases characterized by cytokine release syndrome.

Overcoming Challenges with Biochemical Studies of Selenocysteine and Selenoproteins.

Selenocysteine (Sec) is an essential amino acid that distinguishes itself from cysteine by a selenium atom in place of a sulfur atom. This single change imparts distinct chemical properties to Sec which are crucial for selenoprotein (Sec-containing protein) function. These properties include a lower pKa, enhanced nucleophilicity, and reversible oxidation. However, studying Sec incorporation in proteins is a complex process. While we find Sec in all domains of life, each domain has distinct translation mechanisms. These mechanisms are unique to canonical translation and are composed of Sec-specific enzymes and an mRNA hairpin to drive recoding of the UGA stop codon with Sec. In this review, we highlight the obstacles that arise when investigating Sec insertion, and the role that Sec has in proteins. We discuss the strategic methods implemented in this field to address these challenges. Though the Sec translation system is complex, a remarkable amount of information has been obtained and specialized tools have been developed. Continued studies in this area will provide a deeper understanding on the role of Sec in the context of proteins, and the necessity that we have for maintaining this complex translation machinery to make selenoproteins.

Translating the Untranslatable: DeepL and ChatGPT on Academic Idioms

This research explores the efficacy of two prominent machine translation platforms, DeepL and ChatGPT, in translating academic idioms from English to Indonesian. Academic idioms, situated between discipline-specific jargon and universally understood expressions, pose a challenge for existing translation systems, particularly those rooted in Neural Machine Translation (NMT). The study employs a qualitative descriptive methodology, focusing on translation precision and naturalness, with bilingual experts evaluating translations through a questionnaire, focusing on translation precision and naturalness. The comprehensive analysis involved 50 participants who assessed translations on a scale of accuracy and fluency using Fiederer and O'Brian's (2009) rating scale. The results indicate that both platforms exhibit strengths and weaknesses in terms of accuracy and fluency. While DeepL demonstrates trust in its translation proficiency, ChatGPT receives a more favorable response, especially regarding fluency. Participants preferred ChatGPT for fluency in handling academic expressions, indicating its adaptability. The study also revealed a general agreement among participants regarding the difficulties both platforms encounter in accurately translating academic idioms, emphasizing continuous requirements for improved machine translation. These insights enhance understanding of machine translation's strengths and limitations in academic setting, with implications for future technology development.

Cross-Lingual Short-Text Semantic Similarity for Kannada–English Language Pair

Analyzing the semantic similarity of cross-lingual texts is a crucial part of natural language processing (NLP). The computation of semantic similarity is essential for a variety of tasks such as evaluating machine translation systems, quality checking human translation, information retrieval, plagiarism checks, etc. In this paper, we propose a method for measuring the semantic similarity of Kannada–English sentence pairs that uses embedding space alignment, lexical decomposition, word order, and a convolutional neural network. The proposed method achieves a maximum correlation of 83% with human annotations. Experiments on semantic matching and retrieval tasks resulted in promising results in terms of precision and recall.

TRNA lysidinylation is essential for the minimal translation system found in the apicoplast of Plasmodium falciparum.

For decades, researchers have sought to define minimal genomes to elucidate the fundamental principles of life and advance biotechnology. tRNAs, essential components of this machinery, decode mRNA codons into amino acids. The apicoplast of malaria parasites encodes 25 tRNA isotypes in its organellar genome - the lowest number found in known translation systems. Efficient translation in such minimal systems depends heavily on post-transcriptional tRNA modifications, especially at the wobble anticodon position. Lysidine modification at the wobble position (C34) of tRNACAU distinguishes between methionine (AUG) and isoleucine (AUA) codons, altering the amino acid delivered by this tRNA and ensuring accurate protein synthesis. Lysidine is formed by the enzyme tRNA isoleucine lysidine synthetase (TilS) and is nearly ubiquitous in bacteria and essential for cellular viability. We identified a TilS ortholog (PfTilS) located in the apicoplast of Plasmodium falciparum parasites. By complementing PfTilS with a bacterial ortholog, we demonstrated that the lysidinylation activity of PfTilS is critical for parasite survival and apicoplast maintenance, likely due to its impact on apicoplast protein translation. Our findings represent the first characterization of TilS in an endosymbiotic organelle, advancing eukaryotic organelle research and our understanding of minimal translational machinery. Due to the absence of lysidine modifications in humans, this research also exposes a potential vulnerability in malaria parasites that could be targeted by antimalarial strategies.

Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

A Comparative Study on the Quality of English-Chinese Translation of Legal Texts Between ChatGPT and Neural Machine Translation Systems

A Comparative Study on the Quality of English-Chinese Translation of Legal Texts Between ChatGPT and Neural Machine Translation Systems

On the Nature of the Last Common Ancestor: A Story from its Translation Machinery.

The Last Common Ancestor (LCA) is understood as a hypothetical population of organisms from which all extant living creatures are thought to have descended. Its biology and environment have been and continue to be the subject of discussions within the scientific community. Since the first bacterial genomes were obtained, multiple attempts to reconstruct the genetic content of the LCA have been made. In this review, we compare 10 of the most extensive reconstructions of the gene content possessed by the LCA as they relate to aspects of the translation machinery. Although each reconstruction has its own methodological biases and many disagree in the metabolic nature of the LCA all, to some extent, indicate that several components of the translation machinery are among the most conserved genetic elements. The datasets from each reconstruction clearly show that the LCA already had a largely complete translational system with a genetic code already in place and therefore was not a progenote. Among these features several ribosomal proteins, transcription factors like IF2, EF-G, and EF-Tu and both class I and class II aminoacyl tRNA synthetases were found in essentially all reconstructions. Due to the limitations of the various methodologies, some features such as the occurrence of rRNA posttranscriptional modified bases are not fully addressed. However, conserved as it is, non-universal ribosomal features found in various reconstructions indicate that LCA's translation machinery was still evolving, thereby acquiring the domain specific features in the process. Although progenotes from the pre-LCA likely no longer exist recent results obtained by unraveling the early history of the ribosome and other genetic processes can provide insight to the nature of the pre-LCA world.

Single-Molecule Studies of Cognate and Near-Cognate Elongation in an in vitro Eukaryotic Translation System.

The ribosome plays a central role in translation of the genetic code into amino acid sequences during synthesis of polypeptides. During each cycle of peptide elongation, the ribosome must discriminate between correct and incorrect aminoacyl-tRNAs according to the codon present in its A-site. Ribosomes rely on a complex sequence of proofreading mechanisms to minimize erroneous selection of incorrect aminoacyl-tRNAs that would lead to mistakes in translation. These mechanisms have been studied extensively in prokaryotic organisms, but eukaryotic elongation is less well understood. Here, we use single-molecule fluorescence resonance energy transfer (smFRET) with an in vitro eukaryotic translation system to investigate tRNA selection and subsequent steps during peptide elongation. We compared accommodation of a tryptophan-aminoacyl-tRNA into the ribosomal A-site containing either a cognate or near-cognate codon and unexpectedly found that, following an initial slow sampling event, subsequent near-cognate sampling events proceeded more rapidly than the initial event. Further, we found a strong negative correlation between the concentration of near-cognate aminoacyl-tRNA and the efficiency of tRNA accommodation. These novel characteristics of near-cognate interaction with the eukaryotic ribosome suggest that rejection of a near-cognate tRNAs leads to formation of an altered ribosomal conformation that assists in rejecting subsequent incorrect tRNA interactions.

From rule-based models to deep learning transformers architectures for natural language processing and sign language translation systems: survey, taxonomy and performance evaluation

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation systems.

Developing and Deploying End‐to‐End Machine Learning Systems for Social Impact: A Rubric and Practical Artificial Intelligence Case Studies From African Contexts

ABSTRACTArtificial intelligence (AI) and machine learning have demonstrated the potential to provide solutions to societal challenges, for example, automated crop diagnostics for smallholder farmers, environmental pollution modelling and prediction for cities and machine translation systems for languages that enable information access and communication for segments of the population who are unable to speak or write official languages, among others. Despite the potential of AI, the practical and technical issues related to its development and deployment in the African context are the least documented and understood. The development and deployment of AI for social impact systems in the developing world present new intricacies and requirements emanating from the unique technology and social ecosystems in these settings. This paper provides a rubric for developing and deploying AI systems for social impact with a focus on the African context. The rubric is derived from the analysis of a series of selected real‐world case studies of AI applications in Africa. We assessed the selected AI case studies against the proposed rubric. The rubric and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.

Knowledge Base Prototype Creating with Using Interdisciplinary Metathesaurus.

This article presents our experience in development an ontological model can be used in clinical decision support systems (CDSS) creating. We have used the largest international biomedical terminological metathesaurus the Unified Medical Language System (UMLS) as the basis of our model. This metathesaurus has been adapted into Russian using an automated hybrid translation system with expert control. The product we have created was named as the National Unified Terminological System (NUTS). We have added more than 33 million scientific and clinical relationships between NUTS terms, extracted from the texts of scientific articles and electronic health records. We have also computed weights for each relationship, standardized their values and created symptom checker in preliminary diagnostics based on this. We expect, that the NUTS allow solving task of named entity recognition (NER) and increasing terms interoperability in different CDSS.

Advanced and Safe Synthetic Microbial Chassis with Orthogonal Translation System Integration.

Through the use of CRISPR-assisted transposition, we have engineered a safe Escherichia coli chassis that integrates an orthogonal translation system (OTS) directly into the chromosome. This approach circumvents the limitations and genetic instability associated with conventional plasmid vectors. Precision in genome modification is crucial for the top-down creation of synthetic cells, especially in the orthogonalization of vital cellular processes, such as metabolism and protein translation. Here, we targeted multiple loci in the E. coli chromosome to integrate the OTS simultaneously, creating a synthetic auxotrophic chassis with an altered genetic code to establish a reliable, robust, and safe synthetic protein producer. Our OTS-integrated chassis enabled the site-specific incorporation of m-oNB-Dopa through in-frame amber stop codon readthrough. This allowed for the expression of advanced underwater bioglues containing Dopa-Lysine motifs, which are crucial for wound healing and tissue regeneration. Additionally, we have enhanced the expression process by incorporating scaffold-stabilizing fluoroprolines into bioglues, utilizing our chassis, which has been modified through metabolic engineering (i.e., by introducing proline auxotrophy). We also engineered a synthetic auxotroph reliant on caged Dopa, creating a genetic barrier (genetic firewall) between the synthetic cells and their surroundings, thereby boosting their stability and safety.

Comparative Analysis of Online Translators in the Machine Translation System

The article presents a comparative analysis of various online translators in the machine translation system. The author of the study focused on finding out the effectiveness and accuracy of the translation, identifying the strengths and weaknesses of the three most common online translators: Google Translate, DeepL and Microsoft Translator. The article begins with an overview of current trends in the field of machine translation and the need for online translations based on this context. The research methodology is selected in the article, in particular, the selection of individual online translators for comparison and the criteria for evaluating their work. The researcher analyzes the quality of translations for various types of texts, including common phrases, technical terms, complex sentences, etc. Attention is also paid to different language pairs and ensuring the availability of rarer languages in electronic translation. The article reveals such aspects as accuracy of translation, speed of work, recognition of context and idiomatic expressions, availability of additional functions and capabilities that ensure better translation quality. The author thoroughly highlights the strengths and weaknesses of each system, provides clear information about the use of integrations, available functions and additional capabilities of each translator. In the final part of the article, the author provides conclusions and recommendations regarding the use of online translators in the machine translation system. The research highlights that each electronic translator has its own advantages and limitations, and the choice depends on the specific needs of the user. In addition, the author emphasizes the need for constant updating and improvement of online translators to increase their accuracy and efficiency. This comparative analysis will help simplify the selection of the optimal translation software for users who require high-quality machine translation for their professional activities or everyday needs.

The selective chemical modification of the 6-amino group of adenosine of the premature termination codon induces readthrough to produce full-length peptide in the reconstituted E. Coli translation system

Nonsense mutations in the coding region turn amino acid codons into termination codons, resulting in premature termination codons (PTCs). In the case of the in-frame PTC, if translation does not stop at the PTC but continues to the natural termination codon (NTC) with the insertion of an amino acid, known as readthrough, the full-length peptide is formed, albeit with a single amino acid mutation. We have previously developed the functionality-transfer oligonucleotide (FT-Probe), which forms a hybrid complex with RNA of a complementary sequence to transfer the functional group, resulting in modification of the 4-amino group of cytosine or the 6-amino group of adenine. In this study, the FT-Probe was used to chemically modify the adenosines of the PTC (UAA, UAG, and UGA) of mRNA, which were assayed for the readthrough in a reconstituted Escherichia coli translation system. The third adenosine-modified UAA produced three readthrough peptides incorporating tyrosine, glutamine and lysine at the UAA site. It should be noted that the additional modification with a cyclodextrin only induced glutamine incorporation. The adenosine modified UGA induced readthrough very efficiently with selective tryptophan incorporation. Readthrough of the modified UGA is caused by inhibition of the RF2 function. This study has demonstrated that the chemical modification of the adenosine 6-amino group of the PTC is a strategy for effective readthrough in a prokaryotic translation system.

A narrative inquiry of the government-wide support system for non-English interpretation and translation and government interpreters.

A narrative inquiry of the government-wide support system for non-English interpretation and translation and government interpreters.

Research on Intelligent Translation System of Spoken English Based on Cyclic Neural Network Model

This paper explores the development of an intelligent translation system for spoken English using Recurrent Neural Network (RNN) models. The fundamental principles of RNNs and their advantages in processing sequential data, particularly in handling time-dependent natural language data, are discussed. The methodology for constructing the translation system is outlined, covering key steps such as data preprocessing, model architecture design, and training optimization. The system's performance is evaluated in terms of translation accuracy, fluency, and real-time processing capabilities. The study identifies limitations of the current system and proposes future research directions, including the integration of attention mechanisms, refinement of model architectures, and enhancement of multilingual translation capabilities. Ultimately, this research contributes theoretical insights and practical guidance to the ongoing development of intelligent translation systems for spoken English.

Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.

Language Model Method for Collocation Rules of Parts of Speech in Machine Translation System

With the development of the times, modern society has now entered the Internet of Things (IoT) information age and Machine Translation (MT) plays an important role in increasingly frequent cross-language communication. In recent years, China's artificial intelligence industry has been in a stage of rapid construction, and the scale of its core industries has grown explosively, and a large number of artificial intelligence companies, including issuers, have emerged. Part of speech has always been a major problem in MT. One of the reasons is that there are a large number of multi-category words in Chinese and a large number of polysemy words in English, so part of speech collocation problems account for a large proportion of MT errors, which to some extent affects the credibility and accuracy of the translation. To reduce the error problem in MT of part of speech collocation, this paper used Machine Learning (ML) methods to study the Language Model (LM) of part of speech collocation based on recurrent neural network (NN) and compared it with the traditional statistical LM. In terms of the accuracy rate of the two LMS in the automatic evaluation index of machine translation, the experimental results show that the recursive NN LM established by the ML method had an accuracy rate of 80.42% and 83.57%, respectively, for the part-of-speech matching rules of the IoT machine translation system in the dialogue between Chinese and English and the translation of articles. The accuracy of traditional statistical LM evaluation was 71.29% and 69.52%, respectively. Compared to traditional statistical LM, the accuracy of translation was higher. This showed that the recurrent NN LM reduced the number of errors in the collocation of parts of speech in MT and improved the accuracy and credibility of MT.