Machine Translation Tools Research Articles

Los fraseologismos en el aula de prácticas de traducción: estudio de caso sobre el impacto de los traductores automáticos

Abstract The use of machine translation (MT) systems by undergraduate and postgraduate students has been analysed in different studies (Sycz-Opoń and Gałuskina 2017; Jia et al. 2019; Loock and Léchauguette 2021, to mention a few). An analysis of the German-into-Basque translation of multi-word expressions (Sanz-Villar 2024) observed that students’ dependence on MT systems was very high; i.e., traces of the MT proposals were found in the outputs of translation trainees. To specifically analyse the impact of MT proposals on students’ translations, two tasks were designed in 2021 in which nine students participated. Both tasks consisted of translating an excerpt of a children’s literary text from German into Basque. The first translation task was done with the aid of MT tools and the second was a from-scratch translation where the use of MT systems was not allowed. The goal of the present study is to compare the results of both tasks, focusing on the translation of phraseological units (PUs) to observe the features of students’ translations accomplished with and without the aid of MT tools. Since students used MT outputs during the first translation task, machine-translated texts are also compared to human-translated ones. The results show that the tendency to retain the phraseological character of the original text is stronger in professional translations than in machine-translated texts. In general, reliance on MT system outputs when translating PUs is quite strong among trainee translators. When comparing the results of the two tasks, it was observed that for some students, the number of techniques used to maintain the phraseological level of the original text increased in the translation done without MT. Interestingly, examples of creative translations were found in the translations done without the aid of MT tools.

Research on the Reform of English Translation Course Instruction amidst the Prevailing Trend of Machine Translation

In recent years, machine translation technology has surged forward at an astounding pace, permeating numerous sectors and dramatically elevating both the efficiency and accessibility of translation services. Nevertheless, this technological advancement presents unprecedented challenges to conventional English translation pedagogy. Given the growing ubiquity of machine translation, a critical question arises: How can we refine and enhance the content and methodologies of translation courses to bolster students' translation competencies, especially their translation quality and adaptability when leveraging machine translation tools? This has become an urgent issue within the realm of translation education. Against this backdrop, this article endeavors to investigate novel approaches to reforming English translation course instruction. The objective is to nurture translation professionals who excel not only in language skills but also in the application of technology, thereby fulfilling the heightened expectations for translation work in the future society.

Preparing Translators for the Age of Generative AI: Revising Educational Curricula to Prioritize Essential Skills in an AI-Dominated Landscape

The recent ability of machines to generate text and images that are fluent, coherent, and culturally nuanced is already causing major upheavals in the translation sector. Machine translation tools have exploded in number, sophistication, and quality, and the profession of human translator needs to adapt to work within this new environment and avoid being replaced by it. This paper examines the major changes that have taken place, and which could take place in the near future, and suggests ways of revising translator-training curricula to adapt to these challenges. This is essential at a time when the profession is undergoing profound changes and when all translators and future translators need to be prepared for new skills in the field of post-editing, machine-reading, machine-cultures and multidisciplinarity. After outlining the challenges that AI is causing to the profession and to the norms and values of the translation sector in its current state, the author suggests that translator training should adapt to the new reality of the profession and not to what it used to be. The profession is undergoing profound changes, and all translators and future translators need to find new skills and knowledge in order to continue to work with AI. The article concludes with recommendations on how to design AI-friendly translation programmes that can train students for the post-AI era.

Analysis of Two Translation Applications : Why is DeepL Translate more accurate than Google Translate?

DeepL Translate and Google Translate are two leading machine translation tools. The focus of this research is to analyze the accuracy of translation results provided by DeepL Translation and Google Translation specifically in translating English to Indonesian. This research used a qualitative approach of document analysis and interviews. The advanced neural machine translation technology of DeepL, by utilizing extensive data, enables it to recognize language nuances and provide contextually accurate translations. In contrast, Google Translate, despite having grown to be supported by hundreds of languages, often struggles with complex sentences and idiomatic expressions. DeepL's accuracy and natural-sounding translations make it a top choice for professional and detailed translations. The study concludes that DeepL's focus on quality and accuracy, rather than the breadth of language support, makes it a more reliable translation tool.

Translation Strategies of English Passive Constructions into Arabic: An Analysis of Google Translation Engine Produced Translations Using House’s TQA Model

This study explores the translation of English passive constructions into Arabic, focusing on the prevalent translation shifts and the preservation of the source functions within this process. Utilizing a qualitative methodology, it analyzes passive constructions from an English economic-political article based on House’s (2015) Translation Quality Assessment model. It reviews all passive participles classifying them into verbal and adjectival ones. The research aims to understand how these translation shifts cater to the syntactic and semantic needs of the Arabic language, particularly how machine translation tools like Google Translation engine handle these transformations. Data collected include various English passive constructions and their Arabic translations, providing a foundation for assessing either their adherence to the target language’s norms and preferences or to the literal approach that may lead to affect such preferences and norms. Key findings reveal how Google Translation engine tends to translate according to Arabic norms and produce a natural translation while preserving the source functions. The five translation strategies used by Google Translate when translating passive constructions from English to Arabic in economic-political texts are: 1) adjectival phrases, 2) active structures, 3) passive structures with internal vowel changes, 4) verbless structures with verbal nouns, and 5) verbal phrases with 'tamma'. However, the influence of such an approach may lead to manipulating the source function, which may lead to misunderstandings or to unbalanced power relations.

Exploring the Use of Machine Translation in English-Medium Higher Education Contexts: Perspectives from Omani EFL Students

There has been a significant increase in the use of machine translation by university students as part of their coping strategies for their English-medium study. This practice has been a subject of debate among educators. Some argue that advancements in technology have made machine translation a valuable learning tool, while others remain skeptical, questioning its pedagogical benefits. This study is an attempt to explore Omani EFL students’ perceptions and attitudes regarding the use of machine translation as a coping strategy in their English-medium content learning activities. A qualitative research design, employing an open-ended survey and semi-structured interviews, was used in the study. Thirty-five students enrolled on different English-medium majors at a public Omani university voluntarily participated in the investigation. The data was analysed thematically and inductively. Results showed that all participants reported using machine translation for various learning activities, including looking up the meanings of discipline-specific terminology and translating individual sentences and longer texts from English to Arabic and vice versa. Some participants noted that machine translation tools tend to perform better when translating from Arabic to English compared to translating from English to Arabic. However, participants also highlighted several challenges related to the use of machine translation as a coping learning strategy. The implications for translation pedagogy and research are discussed.

Economic aspects of error identification in semi-structured publications in the state language

Due to the rapid growth of information on the Internet and social networks, research in the field of computational linguistics has become very relevant. The volume of information that people and machines create in natural language needs to be processed, analyzed and verified. Information retrieval systems, dialog systems, and machine translation tools are used for this. The range of automatic text processing systems is very wide, it covers various tasks. Finding errors in texts and words, identifying and correcting incorrect words is one of the most important tasks of natural language processing (NLP). The article provides an overview of semi-structured data, methods and technologies for identifying incorrect words in natural languages. The paper gives an overview of semi-structured data, methods and techniques for detecting incorrect words in natural languages. The aim of the research is to develop an effective approach for detecting and correcting errors occurring in Kazakh-language texts, especially in the context of limited resources and unstructured data. The research includes the use of machine learning techniques as well as economic analysis of the costs of developing and implementing such solutions. The proposed approach facilitates the automation of text verification, which can significantly reduce the cost of manual data processing and improve the quality of information in various spheres, including business and public administration.

Cognitive and sociocultural dynamics of self-regulated use of machine translation and generative AI tools in academic EFL writing

With emerging artificial intelligence (AI) technology, language education has become an increasingly dynamic field where traditional teaching methods intersect with rapidly evolving digital tools, transforming the way English as a Foreign Language (EFL) is taught and learned. This study examines the intersection of cognitive processes and sociocultural contexts in English as a Foreign Language (EFL) classrooms, focusing on 79 learners enrolled in a compulsory EFL course at a Japanese university and their self-regulated use of AI tools like ChatGPT and Google Translate for writing tasks. Employing a mixed-methods approach, this study investigates learners' evolving perceptions and highlights the interplay between their cognitive engagement and sociocultural interactions within the classroom setting. Quantitative data from pre- and post-surveys reveal learners’ attitudes towards AI-assisted writing, while qualitative insights are drawn from their self-reported experiences. Findings indicate that while the students perceived that their writing outcomes have improved by using AI tools for editing, the role of instructors for providing proper guidance and training on the technology, as well as the collocative learning with peers cannot be replaced by the AI tools.

Machine Translation and NLP Tools: Developing and Refining Language Technologies for African Languages

Machine Translation (MT) and Natural Language Processing (NLP) tools have significantly evolved over the past few decades, leading to notable advancements in the field of language technology. However, most of these developments have been concentrated on languages with abundant data, such as English, Chinese, and European languages. African languages, with their unique linguistic structures and vocabularies, remain underrepresented in the MT and NLP landscape (Joshi et al., 2020). This article explores the challenges and opportunities in creating and refining machine translation models and NLP tools tailored to the unique structures of African languages. It discusses the specific linguistic features that set African languages apart, reviews the current state of MT and NLP tools for these languages, and outlines strategies for developing more effective models that cater to the diverse linguistic landscape of Africa.

A Study on How Globalization Influences Translation Practices and the Demand for Translators in Various Industries

This study investigates the impact of globalization on translation practices and the demand for translators across various industries. Utilizing a mixed-methods research design, the study combines quantitative surveys and qualitative interviews with professional translators, translation agency managers, and industry experts. The findings reveal that technological advancements, particularly machine translation (MT) and computer-assisted translation (CAT) tools have significantly altered translation workflows, increasing efficiency while necessitating careful quality control. The demand for localization services has surged, especially in the tech industry, reflecting a broader trend towards culturally adapted content in global markets. Additionally, the study highlights a notable shift towards freelancing and remote work among translators, driven by the desire for flexibility but accompanied by challenges such as job stability and securing consistent work. Emerging challenges identified include maintaining translation quality amidst high workloads, adapting to rapidly evolving technologies, and navigating cultural differences to ensure culturally appropriate translations. Economic pressures, such as low rates and payment issues, further complicate the landscape for professional translators. These findings align with existing literature, emphasizing the need for continuous adaptation, professional development, and strategic approaches to address the dynamic and evolving nature of the translation industry in a globalized context. The study provides valuable insights into the current state of the translation industry, highlighting the importance of cultural competence, technological proficiency, and economic resilience for translators operating in a globalized world. Future research should explore the long-term impacts of these trends and the evolving role of translators in a rapidly changing global market.

Recognition and normalization of multilingual symptom entities using in-domain-adapted BERT models and classification layers.

Due to the scarcity of available annotations in the biomedical domain, clinical natural language processing poses a substantial challenge, especially when applied to low-resource languages. This paper presents our contributions for the detection and normalization of clinical entities corresponding to symptoms, signs, and findings present in multilingual clinical texts. For this purpose, the three subtasks proposed in the SympTEMIST shared task of the Biocreative VIII conference have been addressed. For Subtask 1-named entity recognition in a Spanish corpus-an approach focused on BERT-based model assemblies pretrained on a proprietary oncology corpus was followed. Subtasks 2 and 3 of SympTEMIST address named entity linking (NEL) in Spanish and multilingual corpora, respectively. Our approach to these subtasks followed a classification strategy that starts from a bi-encoder trained by contrastive learning, for which several SapBERT-like models are explored. To apply this NEL approach to different languages, we have trained these models by leveraging the knowledge base of domain-specific medical concepts in Spanish supplied by the organizers, which we have translated into the other languages of interest by using machine translation tools. The results obtained in the three subtasks establish a new state of the art. Thus, for Subtask 1 we obtain precision results of 0.804, F1-score of 0.748, and recall of 0.699. For Subtask 2, we obtain performance gains of up to 5.5% in top-1 accuracy when the trained bi-encoder is followed by a WNT-softmax classification layer that is initialized with the mean of the embeddings of a subset of SNOMED-CT terms. For Subtask 3, the differences are even more pronounced, and our multilingual bi-encoder outperforms the other models analyzed in all languages except Swedish when combined with a WNT-softmax classification layer. Thus, the improvements in top-1 accuracy over the best bi-encoder model alone are 13% for Portuguese and 13.26% for Swedish. Database URL: https://doi.org/10.1093/database/baae087.

The "machine translation + post-machine translation editing" model on the YiCAT platform: using the example of the discipline "Stylistic post-machine text editing" for first-year graduate students of the Higher School of Translation of Moscow State University

In this article, the author analyzes the process of machine translation and post-machine editing by Chinese students in classes on the discipline "Stylistic post-machine text editing" using the YiCAT translation platform, which uses a model of combining machine translation and post-machine editing in order to organize the educational process and allocate study time. In 2017, the Russian government approved the "Digital Economy Plan of the Russian Federation", as a result of which the digitalization process gradually penetrated into all scientific spheres. The author of the article concludes that Russian universities should pay more attention to teaching machine translation and post-machine translation editing. In addition, the article presents the idea that the translation departments of modern universities should focus on the introduction and application of translation technologies and the latest translation tools in the framework of teaching translation skills. There are only a few hundred articles in Russia dealing with the specifics of machine translation. Most of them are devoted to the study of existing limitations, development opportunities and ways to improve machine translation systems. At the same time, there are extremely few articles dealing with the features of post-machine editing of the translation text. The number of such articles does not exceed several dozen. Thus, there are many challenges in the field of post-machine translation editing research that should be addressed as soon as possible so that the field of post-machine translation editing can correspond to the era of artificial intelligence. At the same time, Russian universities should pay attention to the teaching of disciplines involving machine translation tools and post-machine editing skills. Large translation faculties should actively use translation technologies and machine translation tools in the classroom, add new academic disciplines implying the familiarization and application of such technologies and tools by students, as well as integrate such disciplines into the framework of existing training programs for specialists in the field of translation. All this will allow universities to form a reliable basis for the education of modern and qualified specialists who meet the requirements of the new era.

Le futur du métier de traducteur face à la révolution numérique : préoccupations et perspectives pour les bilingues

The Future of The Translation Profession in The Age of Digital Revolution: Concerns and perspectives for bilingual professionals The digital revolution has caused a significant shift in labour market policies across all sectors. The rapid advancement and widespread adoption of software and IT tools have led to an increased risk of job displacement for skilled professionals, as these tools enable even non-experts to perform tasks traditionally handled by specialists. This study explores the potential threat to employment faced by professional translators due to these technological advancements. The rise of artificial intelligence and machine translation (MT) has encouraged many bilingual individuals to enter the translation field, a trend that has raised concerns among linguistic researchers. These technological developments have also cast doubt on the value of language experts. This research addresses key questions: To what extent is the future of professional translators at risk? Do professional translators face significant competition in the Information Age? To answer these questions, this study employed both quantitative and qualitative research methods, including field data collection from bilinguals and professional translators. Questionnaires were administered first to bilinguals to gauge their motivations and then to professional translators to understand their concerns and perspectives. The data analysis provided clear insights that supported the study's hypotheses. The research suggests that professional translators may face substantial unemployment risks in the future. This is largely attributed to the increasing involvement of bilinguals in the field, driven by the proliferation of machine translation tools. The study highlights the relevance of connectivism and job prospection theories in understanding the challenges faced by professional translators and in addressing the rise of bilinguals in the workforce.

Accuracy Analysis of DeepL: Breakthroughs in Machine Translation Technology

This study examines the accuracy and technological innovations of DeepL, a prominent machine translation tool. Through a comprehensive literature review, we analyze DeepL's performance compared to other translation systems, exploring its advanced neural network architecture and training methods. Key findings indicate that DeepL consistently outperforms other tools in BLEU scores and human evaluations, particularly excelling in handling context, idiomatic expressions, and specialized terminology. The research highlights DeepL's use of the Transformer model, diverse training data, and techniques like transfer learning and data augmentation. Practical applications across academic, professional, and educational sectors are discussed, with special emphasis on how DeepL benefits students and teachers by facilitating multilingual learning, enhancing comprehension of foreign texts, and assisting in accurate translation of academic materials. User feedback underscores DeepL's accuracy and user-friendly features. While demonstrating significant advancements in machine translation technology, this study also acknowledges areas for potential improvement, contributing to the ongoing development of AI-driven language solutions.

Using learner corpus data for grammatical accuracy development in written productions

Abstract This study investigates the differential effect of various noticing activities on grammatical accuracy development in EFL learners’ written productions. We focus on different types of noticing activities based on an error-tagged learner corpus and report on effective practical experiments involving learner corpus data. A pretest/posttest quasi-experimental design is used with three experimental groups (receiving different treatments) and one control group. Error frequencies, at both group and individual levels, and proportions of learners producing errors on three specific error types (articles, verb tense, verb agreement) are compared. Our results suggest that accuracy in the use of articles and verb agreement could be more easily fostered through the comparison of learner output with native data (the BNC, in our case). As for verb tenses, the impact of a more traditional form of corrective feedback seems greater while the use of online machine translation tools does not seem to foster much accuracy development.

TRACING THE FOOTPRINT OF WORLD ENGLISHES ON MACHINE TRANSLATION TOOLS

With the rise in the population who uses English as a Foreign or Second language, World Englishes has become more focus of English language teaching field specialists. As a consequence, English varieties which are present in supplementary tools used in the process of language teaching and learning took on a new meaning. As one of the commonly used supplementary tools is machine translation tools, the current study aims to explore the machine translation outputs tendency considering grammar-based, spelling-based, and lexical variations between varieties of Englishes regarding Google Translate outputs. Source texts were extracted from Turkish National Corpus. A total of seventy-three Turkish to English machine translation outputs were examined regarding two dominant varieties, American English and British English, with the aim of figuring out the machine translation tendency of translation outputs. The analysis of the research showed that Google Translate provides users with mostly American English compared to British English.

Indonesian EFL Students’ Perceptions of DeepL Machine Translation Tool: Utilization, Advantages, and Disadvantages

Indonesian EFL Students’ Perceptions of DeepL Machine Translation Tool: Utilization, Advantages, and Disadvantages

The future of language education: Teachers’ perceptions about the surge of AI writing tools

Machine translation (MT) tools have been causing language educators distress since 2006, when GoogleTranslate was first launched. With its ever-increasing efficiency, it has become a reference for language learners looking for shortcuts, and the bane of second language (L2) educators concerned with academic honesty. Henshaw (2020) notes that, over the years, educators have developed a range of approaches for dealing with MT, from incorporating translation activities to outright bans. Educators' anxiety over the surge of new tools is completely normal. “When confronted with a new phenomenon, especially one that can be perceived as an existential threat, a common human reaction is to wish the problem away” (Urlaub & Dessein, 2022, p. 52). While many would seem to wish away ChatGPT and other AI writing tools, they are here to stay. Proliferating at an astronomical rate (ChatGPT garnered over 100 million users just two months after its release) and armed with the ability to imitate natural speech in any genre and language, these tools are poised to accelerate the academic disruption MT tools started. The goal of this study is to examine the perceptions and solutions of L2 educators facing a tool that is arguably more powerful than GoogleTranslate. We surveyed over 100 language instructors, asking questions about their ideas for AI-proofing assignments, their policies, and their perceptions of how this tool will impact the profession. Data was collected through an anonymous survey and analyzed through a constructivist lens. Preliminary results show a mixture of sentiments toward the use of these tools in the language classroom, with some fearful for their job security and others excited about the tools’ potentialities. The second part of this article looks towards the future and seeks to provide an overview of faculty approaches to AI tools, including ideas for assignments and policies.

Navigating Ideology and Policy: A Corpus-Based Analysis of Translation Practices

This paper investigates the complex dynamics between language policy, ideological influences, and the use of machine translation (MT) tools in the translation of multilingual documents within the European Union (EU). A corpus-based analysis was employed to examine how language policies and ideological preferences shape translation practices, particularly focusing on governmental and marketing materials. The study reveals significant tendencies towards lexical standardization and syntactic homogenization, driven by the efficiency-oriented ideologies embedded within language policies. Furthermore, it highlights the ethical and practical challenges posed by MT, including cultural flattening and the potential undermining of local nuances crucial for effective communication. The interplay between compliance and resistance by human translators under restrictive language policies is also explored, showing how translators navigate between policy constraints and their own professional judgments. This paper contributes to the broader discourse on the impact of technological advances on translation practices, emphasizing the need for a balanced approach that respects linguistic diversity while leveraging technological efficiencies.

Transcending Linguistic Boundaries: A Technical Exploration of The Evolution and Future Trajectory of Corpus-Based Machine Translation

This review paper presents a comprehensive examination of the evolution, current state, and future perspectives of machine translation (MT). The field of MT, encompassing the automatic translation of text from one language to another, has undergone significant transformations from rule-based methods to statistical models, and more recently, to neural network-based approaches. By synthesizing current research and technological progress, this paper aims to provide a detailed understanding of the dynamic landscape of machine translation and its future directions. In today's globalized world, language barriers remain a significant obstacle to accessing information. Relying solely on human translators often fails to meet the growing demand for translation services, which is why Machine Translation (MT) tools are becoming increasingly popular. These tools have the potential to address this issue effectively. As a result, research in MT is expanding rapidly, and new paradigms are continually being developed. This paper presents a systematic literature review to identify the most widely used MT systems, their architectures, the quality assessment methods used to evaluate them, and which systems deliver the best results.