Translation Software Research Articles

Machine-Learning Applications in Thrombosis and Hemostasis.

The use of machine-learning (ML) algorithms in medicine has sparked a heated discussion. It is considered one of the most disruptive general-purpose technologies in decades. It has already permeated many areas of our daily lives and produced applications that we can no longer do without, such as navigation apps or translation software. However, many people are still unsure if ML algorithms should be used in medicine in their current form. Doctors are doubtful to what extent they can trust the predictions of algorithms. Shortcomings in development and unclear regulatory oversight can lead to bias, inequality, applicability concerns, and nontransparent assessments. Past mistakes, however, have led to a better understanding of what is needed to develop effective models for clinical use. Physicians and clinical researchers must participate in all development phases and understand their pitfalls. In this review, we explain the basic concepts of ML, present examples in the field of thrombosis and hemostasis, discuss common pitfalls, and present a methodological framework that can be used to develop effective algorithms.

Linguistic sensitivity in family therapy: A review of language adaptation strategies for enhancing therapeutic engagement

Language plays a pivotal role in family therapy, shaping the therapeutic process and influencing client engagement and outcomes. This review explores the significance of linguistic sensitivity in family therapy and examines various language adaptation strategies aimed at enhancing therapeutic engagement. Drawing upon existing literature, this paper synthesizes key findings and offers insights into effective approaches for addressing linguistic diversity within therapeutic contexts. Understanding the cultural and linguistic backgrounds of clients is essential for promoting inclusivity and facilitating effective communication in family therapy. Cultural competence encompasses not only an awareness of diverse cultural norms and values but also proficiency in navigating linguistic nuances. By recognizing the impact of language on therapeutic interactions, therapists can foster a more inclusive and empowering therapeutic environment. The review identifies several language adaptation strategies that therapists can employ to enhance therapeutic engagement with diverse client populations. These strategies include linguistic matching, which involves adjusting language patterns and communication styles to resonate with clients' linguistic preferences. Additionally, the use of culturally relevant metaphors, idioms, and storytelling techniques can facilitate deeper rapport and understanding between therapists and clients. Furthermore, the review discusses the importance of employing interpreters and bilingual therapists to bridge language barriers effectively. It emphasizes the need for clear communication protocols and the importance of training therapists in language adaptation techniques to ensure ethical and effective practice. Moreover, the review highlights the role of technology in language adaptation, such as the use of translation software and teletherapy platforms with multilingual capabilities. Leveraging technology can broaden access to therapy and support linguistic diversity in therapeutic settings. Linguistic sensitivity is fundamental to promoting inclusivity and effectiveness in family therapy. By adopting language adaptation strategies and embracing linguistic diversity, therapists can cultivate a more responsive and culturally competent approach to working with diverse families. This review underscores the importance of ongoing research and training initiatives to further enhance linguistic sensitivity within the field of family therapy.

Research on Automatic Proofreading Algorithm for English Translation Based on Neural Networks

In this proposed study, we explore the development and implementation of an innovative proofreading algorithm aimed at enhancing the accuracy of English translation. This algorithm leverages the capabilities of Convolutional Neural Networks (CNN) integrated with a fuzzy logic approach, offering a novel perspective in the realm of linguistic accuracy and consistency in translations. The core objective of this research is to address the prevalent challenges in automatic translation, such as context misinterpretation and semantic errors, by employing a fuzzy-based CNN model. This model is meticulously trained and tested using a diverse dataset of English translations, enabling it to learn and adapt to various linguistic nuances. Our results demonstrate a significant improvement in the proofreading accuracy, outperforming existing methods in terms of efficiency and reliability. The research highlights the potential of combining neural networks with fuzzy logic to create more sophisticated and context-aware translation tools. While our findings mark a considerable advancement in automatic translation proofreading, we also acknowledge the scope for further enhancements. Future work could involve refining the algorithm, expanding its applicability to other languages, and integrating it into real-world translation software. This research contributes to the evolving landscape of automated translation, presenting a promising solution for achieving higher translation fidelity.

A Comparative Study of Artificial Intelligence and Translation Software in Chinese-English Translation: A Focus on Literary and Technical Texts

A Comparative Study of Artificial Intelligence and Translation Software in Chinese-English Translation: A Focus on Literary and Technical Texts

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.

Deep learning model of semantic direction exploration based on English V+able corpus distribution and semantic roles

In order to improve English learning efficiency, this paper constructs a deep learning model of semantic orientation exploration based on English V+able corpus distribution and semantic roles. This article combines the practical needs of English learning and establishes an ILP model with the optimization objective of minimizing spectrum resource occupation. A traffic grooming based time aware multipath RSA algorithm (HMRSA-TG) is proposed to solve the standardization problem of English speech recognition. To improve the system efficiency of intelligent English learning systems, a traffic grooming based time aware multipath RSA algorithm (HMRSA-TG) is proposed. Through research, it has been verified that the semantic orientation exploration deep learning model based on the distribution of semantic roles in English V+able corpora can effectively improve the effectiveness of English speech learning. The corpus model proposed in this paper can provide a reliable benchmark database for many speech problem learners and play an important role in English translation software in recognizing input speech with different accents

Towards a Digitalised Criminal Justice System: Lessons from Poland

The Covid-19 pandemic has expedited technological advancements within the Polish criminal justice system, notably through the integration of remote communication tools for legal proceedings. This paper examines the introduction and impact of remote hearings, including those for detention on remand, arguing for the expansion of remote hearings beyond cases where the defendant is in custody. It highlights the existing legal framework's limitations, such as the conditional telephone contact between a defendant and their defense counsel, which infringes on the right to defense due to the lack of confidentiality and effectiveness in legal assistance. Furthermore, the paper discusses the potential benefits of digitizing criminal proceeding files, including improved efficiency, accessibility, and the safeguarding of the fair trial principle. Despite potential drawbacks like digital exclusion and security concerns, the implementation of a unified digital system across all courts is proposed to facilitate data exchange and streamline proceedings. Additionally, the paper addresses the limitations of replacing human interpreters with automated translation software in criminal proceedings, emphasizing the necessity of human oversight to ensure fair trial guarantees. The study suggests a hybrid approach to translation that combines machine translation with human verification, drawing parallels with GDPR provisions for human intervention in automated decision-making processes.

‘Little Arabia’ on Buddhist land: Exploring the linguistic landscape of Bangkok’s ‘Soi Arab’ enclave

Abstract Never formally colonised by Western powers, Thailand is a rapidly developing nation in Southeast Asia. To outsiders, the assumption might be that Thailand is a homogenous Thai-speaking Buddhist country. However, such over-simplistic views ignore diversity and the existence of de facto multilingualism and multiculturalism on the ground. This linguistic landscape (LL) study explores a unique and sociolinguistically compelling area called ‘Little Arabia’ (Soi Arab) in the heart of Bangkok, where elements of both Islamic and Buddhist civilisations meet. The existence of this Middle Eastern enclave creates a sense of contrasts on linguistic, religious, and cultural levels. Because of globalisation and spurred by (medical and sex) tourism, this area boasts a number of halal restaurants, hotels, travel agencies, stores, pharmacies, and clinics. This gives rise to an interesting sociolinguistic ecology, featuring an ‘ethnic’ economy which caters to the needs of tourists and businessmen hailing from the Arab world. Drawing on authentic photographic data, this study explores how various linguistic practices give the area a unique identity. More specifically, we reveal how various languages (including Arabic, Thai, and English) are mobilised and combined in ways that illustrate translation practices evidenced within the enclave for particularised marketing, commercial, and communication purposes. Theoretically and conceptually, the term ‘machine-translated multilingualism’ is coined to capture the growing trend for businesses to resort to translation software for multilingual communication, which may result in non-standard translations and orthographic forms. This study contributes to a growing body of sociolinguistic research examining LLs in global cities and commercial hubs in the Global South, particularly those arising from ‘South-South’ population flows.

Data mining based on computer game search algorithm in Japanese E-learning and multi context translation

With the continuous expansion of the translation market, users’ demand for translation tools such as computer software is also increasing, resulting in various machine translation technologies and methods. Japanese translation, as one of the important components of Chinese publicity work, has strong professionalism and complexity. In practice, how to effectively improve the translation quality of Japanese translation models using computer technology has always been a research issue of great concern. Based on this, we propose a game search algorithm and use it as a prototype to improve the algorithm. Then, a data mining model was established that can effectively improve search performance. This model solves the above problems by introducing genetic algorithms and dynamic programming theory. At the same time, in order to further improve the efficiency of existing data mining algorithms, we need to explore more efficient methods and strategies. After experimental verification, the four data mining models proposed in this article perform the best in numerical fitting. Overall, in Japanese multi context translation systems, the use of data mining techniques based on computer game search algorithms can provide more accurate and matching related translations. Compared to traditional Japanese translation software, it has higher quality and efficiency.

Empowering Communication: A Deep Learning Framework for Arabic Sign Language Recognition with an Attention Mechanism

This article emphasises the urgent need for appropriate communication tools for communities of people who are deaf or hard-of-hearing, with a specific emphasis on Arabic Sign Language (ArSL). In this study, we use long short-term memory (LSTM) models in conjunction with MediaPipe to reduce the barriers to effective communication and social integration for deaf communities. The model design incorporates LSTM units and an attention mechanism to handle the input sequences of extracted keypoints from recorded gestures. The attention layer selectively directs its focus toward relevant segments of the input sequence, whereas the LSTM layer handles temporal relationships and encodes the sequential data. A comprehensive dataset comprised of fifty frequently used words and numbers in ArSL was collected for developing the recognition model. This dataset comprises many instances of gestures recorded by five volunteers. The results of the experiment support the effectiveness of the proposed approach, as the model achieved accuracies of more than 85% (individual volunteers) and 83% (combined data). The high level of precision emphasises the potential of artificial intelligence-powered translation software to improve effective communication for people with hearing impairments and to enable them to interact with the larger community more easily.

A story of social entrepreneurship through the creation of the Hopeboots project

Research methodology This case was developed by using primary data collected from two separate one on one interviews, a panel interview in which Josee was featured and secondary data collected from news articles and publications featuring Josee. Information specific to Atikuss’ offerings was found through the Atikuss website. A translation software was used to understand many of the articles about Josee, as many were in French. Case overview/synopsis Atikuss (meaning young caribou in Innu) is the sustainable business founded by Josee LeBlanc, an Indigenous woman from Northern Quebec. As a workshop-boutique, Attikuss offers a diverse selection of hand-made traditional Indigenous items from her own Indigenous culture. Hopeboots is a project run through Atikuss which allows customers to create their own Mukluks while learning about Indigenous culture and the story behind every design. When starting her business, Josee learned that the women making mukluk boots were not earning a livable wage for their work. Her dilemma when creating a sustainable business was whether to increase the beaders wages to a fair wage, costing her and the consumer more, or maintaining the status quo by continuing to pay the beaders less then five dollars an hour. Josee’s decision to increase wages generated opportunities and increased well being through social investments in her community. This decision considers the cost to many stakeholders and offers an Indigenized perspective to entrepreneurship. This case is relevant to Indigenous entrepreneurship, sustainability, social innovation, business ethics, and corporate social responsibility. Complexity academic level This case is targeted toward university-level students and can be relevant to graduate-level students as well.

Automated translation accurately translates recorded pediatric neurosurgery clinic conversations between Spanish and English.

The purpose of this study is to analyze an automated voice to text translation device by reporting the translation accuracy for recorded pediatric neurosurgery clinic conversations, classifying errors in translation according to their impact on overall understanding, and comparing the incidence of these errors in English to Spanish vs. Spanish to English conversations. English and Spanish speaking patients at a single academic health system's outpatient pediatric neurosurgery clinic had their conversations recorded. These recordings were played back to a Google Pixel handheld smartphone with Live Translate voice to text translation software. A certified medical interpreter evaluated recordings for incidence of minor errors, errors impacting understanding, and catastrophic errors affecting patient-provider relationship or care. Two proportion t-testing was used to compare these outcomes. 50 patient visits were recorded: 40 English recordings translated to Spanish and 10 Spanish recordings translated to English. The mean transcript length was 4244 ± 992 words. The overall accuracy was 98.2% ± 0.5%. On average, 46 words were missed in translation (1.09% error rate), 31 understanding-altering translation errors (0.73% error rate), and 0 catastrophic errors were made. There was no significant difference in English to Spanish or vice versa. Voice to text translation devices using automatic speech recognition accurately translate recorded clinic conversations between Spanish and English with high accuracy and low incidence of errors impacting medical care or understanding. Further study should investigate additional languages, assess patient preferences and potential concerns with respect to device use, and compare these devices directly to medical interpreters in live clinic settings.

Application of ELAN in Human-Machine Mutual Translation of English for the News

The translation of English news means the process of converting news articles published in English into another language. These articles may also be referred to as English news. This process comprises understanding the substance of the source article and accurately translating its tone and intent into the target language. A human translator or machine translation software can be used to perform the translation manually. In the disciplines of software globalization and technological translation, the Human-Machine Mutual Translation technique has grown significantly with the advancement of civilization and the quickening growth of research and innovation. Because of this practical need, translators have begun to focus more on machine translation and have conducted some productive research in this area. As a result, we suggested the EUDICO Linguistic Annotator (ELAN) in Human-Machine mutual translation of English for the news. EUDICO stands for European Distributed Corpora Project. The Max Planck Institute for Psycholinguistics created the EUDICO Linguistic Annotator as a software program for the linguistic annotation of language data. For this study, the English news dataset was used. The data were pre-processed by using normalization. The results were evaluated using performance metrics such as translation quality, precision, BLEU, recall, and computation time. The findings show that the recommended procedure provides an accurate translation of English news when compared to previously used methods. The results demonstrated that translation quality and computation time of proposed model are 95% and 60s respectively.

Development of Offline Translation Software for English to Hindi

Development of Offline Translation Software for English to Hindi

Design and Implementation of an English-Chinese Translation System Based on Crawler

This system uses crawler technology to obtain relevant data through simulation translation software, and it saves these data to the local. It can finish accurate and real-time translation of Chinese and English. The system is very practical and it provides convenience for the scenes that need to be translated in daily life.

Report on English-Chinese Translation Practice Based on Trados Computer Aided Translation: A Case Study of Procedure in Civil Cases in the U.S. Courts

This report focuses on the translation practice of parts of the text from "Procedure in Civil Cases in the U.S. Courts" using the Trados translation software. By comparing and analyzing the application of Trados in the translation process, this study aims to reveal the advantages and limitations of using computer aided translation (CAT) tools in legal text translation. The research finds that Trados significantly aids in the field of legal translation; however, its effectiveness is still limited by the translator's grasp of legal expertise. Therefore, this paper intends to offer practical suggestions and experience summaries for legal text translation and to discuss the pros and cons of CAT tools in specific fields. Through an in-depth analysis of Trados usage and a detailed examination of the translation practice results, this paper aims to provide feasible recommendations for improving the quality of legal text translation and to serve as a reference for the application of CAT tools in the legal field.

Systematic Review of English/Arabic Machine Translation Postediting: Implications for AI Application in Translation Research and Pedagogy

The twenty-first century has witnessed an extensive evolution in translation practice thanks to the accelerated progress in machine translation tools and software. With the increased scalability and availability of machine translation software empowered by artificial intelligence, translation students and practitioners have continued to show an unwavering reliance on automatic translation systems. Academically, there is little recognition of the need to develop machine translation skillsets amongst translation learners in English/Arabic translation programs. This study provides a systematic review of machine translation postediting with reference to English/Arabic machine translation. Using the Preferred Reporting Items for Systematic Review and Meta-Analysis, the paper reviewed 60 studies conducted since the beginning of the twenty-first century and classified them by different metrics to identify relevant trends and research gaps. The results showed that research on the topic has been primarily prescriptive, concentrating on evaluating and developing machine translation software while neglecting aspects related to translators’ skillsets and competencies. The paper highlights the significance of postediting as an important digital literacy to be developed among Arabic translation students and the need to bridge the existing research and pedagogic gap in MT education.

Analysis of Feature Extraction Model for Machine Korean Translation Judging System

Language translation is becoming increasingly widespread and diversified as internationalization and information technology advance significantly. Nonetheless, there are cultural variations across different nations, and there will unavoidably be some variations brought on by translation software. While the machine translation system has attained a high level of development, the issues with the older machine translation methods still remain and cannot be fully resolved. The efficiency of Korean-Chinese translation must thus be improved by creating a more effective model for converting translation information. This has significant implications for the development of international relationships, commercial interactions, cultural development, and commerce between China and South Korea. The translation is the rendering of words into other languages so that they may be understood by both individuals. Nevertheless, there are several flaws and inconsistencies in the present Korean-to-Chinese translational information processing method. Thus, this study suggests a Diverse Deep Embedding Data Accumulation (DDEDA) algorithm for the translation framework. We utilized the Korean-Chinese corpus dataset for the research. Utilizing translation accuracy, precision, recall, error rate, and translation time, the performance of the suggested approach was examined and compared with previously employed methodologies. The findings demonstrate that the proposed method has taken 43s for translation.

Nursing and midwifery students’ ethical views on the acceptability of using AI machine translation software to write university assignments: A deficit-oriented or translanguaging perspective?

This paper focuses on tertiary English as an additional language (EAL) students' ethical choices, and the factors impacting on them, when deciding whether to engage with artificially-intelligent (AI) machine translation (MT) tools for the writing of university assignments. It also investigates how student responses align with either deficit-oriented or translanguaging theoretical perspectives. Via semi-structured interviews, the voices of 23 EAL nursing and midwifery students indicate an array of ethical positions which are based on three key areas of consideration: 1) ownership of language and ideas; 2) fairness and respect; and 3) personal growth. The study highlights the scalar, strategic and dynamic nature of students’ ethical decisions and shows that questions of ethicality tap into individual, social and institutional constructs of fairness and respect, skills recognition, lifelong learning and language dominion. The findings also indicate that discussions of fairness should focus not only on differences between non-EAL and EAL students, but also inequalities within EAL cohorts. Student responses provide evidence of both deficit-oriented and translanguaging perspectives. The researchers call for universities to create clear policies concerning use of MT that recognise the levels of reflection that students engage in when writing their assignments and value the full linguistic repertoires that students bring to global educational settings.

GlobalLingua: Empowering Multilingual Access to YouTube Video Transcripts with Automated Translation

GlobalLingua removes language barriers that block online video content worldwide. This application lets content creators, educators, and individuals obtain and translate YouTube transcripts. The software uses clever technology to simplify user-provided YouTube video URLs. PyTube quickly gets videos, titles, and transcripts using the YouTube_transcript_api. The software also easily converts English texts into the user-specified language with the Google Convert API. It powerfully removes temporary files and processes movies to increase speed and reliability. Automatic YouTube video transcript translation software increases global content accessibility, knowledge exchange, and cross-cultural communication. The application automates transcript extraction and translation, enabling multilingual content and digital inclusion