Machine Translation Technology Research Articles

Cognitive Load and Translation Accuracy in Technology Assisted Simultaneous Interpreting Enabled by Artificial Intelligence

This paper uses eye-tracking research to examine the cognitive load and translation accuracy of simultaneous interpretering with and without technology-assisted in the direction of Chinese-to-English translation and to explore the moderating role of simultaneous interpreting ability. It was found that the new type of simultaneous interpreting assisted by speech recognition technology and machine translation technology can significantly reduce the cognitive load of student interpreters and improve the accuracy of translation. However, the advantages of technology-assisted simultaneous interpreting are not significant enough for professional interpreters compared with student interpreters. This paper explains the results of the study from the perspectives of memory pressure, bilingual switching pressure, and the “ceiling effect”.

Construction of Algorithms for English Learners to Read Foreign Literature Based on Machine Translation Technology

The construction of algorithms for English learners to read foreign literature based on machine translation technology involves several key steps. Initially, the algorithm utilizes machine translation to convert the foreign text into English, ensuring accessibility for learners. Next, it employs natural language processing (NLP) techniques to enhance the readability and comprehension of the translated text, such as simplifying complex sentences, clarifying ambiguous phrases, and providing annotations or explanations for cultural or linguistic nuances. Additionally, the algorithm may incorporate adaptive learning mechanisms to personalize the reading experience, adjusting the difficulty level and content based on the learner's proficiency and preferences. This paper explores the development and implementation of algorithms designed to facilitate English learners' comprehension of foreign literature using machine translation technology. Leveraging Probability Path analysis and classification techniques, investigate the effectiveness of various algorithms in improving translation accuracy, fluency, and comprehension for English learners. Through the Probability Path analysis, we delve into the complexities of language structures, translation probabilities, learner interaction, and linguistic adaptation, illuminating the intricate dynamics of machine translation in the context of language learning. Additionally, classification results provide insights into the performance of different algorithms in translating diverse literary genres, showcasing their potential to support English learners in comprehending foreign texts. The findings reveal translation accuracies ranging from 87.8% to 92.0%, with corresponding fluency scores ranging from 4.0 to 4.6. Additionally, algorithms result in comprehension improvements of 23% to 30% for English learners.

The Impact of Machine Translation on the Development of Tourism Translation From the Perspectives of Translators and Experts in Saudi Arabia

Regardless of its distinction for touring and tourism communication, the subject of machine translation has not been dealt with regarding tourism investigation. This study aims to bridge this gap in knowledge by exploring machine translation (MT) technology in the development of tourism translation from the perspectives of translators and translation experts in Saudi Arabia. To attain the three objectives of the study, the researcher applied the quantitative approach. For data gathering, a 12-item opinion poll was disseminated among a random sample group that consisted of 80 translators and translation experts who were selected at random from several Saudi universities, top translation services companies, and tourism organizations during the current academic year 1445/AH/2024 CE. The findings of this study discovered that many translators and translation experts had positive perspectives on utilizing machine translation (MT) technology in the development of tourism translation around the world today in general and Saudi Arabia in particular. Moreover, the perspectives of these translators and translation experts on the difficulties and intricacy confronted in applying machine translation (MT) technology in the tourism translation industry were highly positive. Over half of the participants (51.42%) perceived that machine translation (MT) can help overcome obstacles encountered by translators in the field of tourism. The findings revealed that translators and translation experts in the realm of the tourism industry have a promising future thanks to the relationship between machine translation (MT) and tourism translation.

Construction of Algorithm for English Learners to Read Foreign Literature Based on Machine Translation Technology

This study focuses on the development of an algorithm aimed at assisting English learners in comprehending foreign literature through the utilization of machine translation technology. With the growing importance of multilingual proficiency in a globalized world, the ability to read and understand literature in foreign languages is increasingly valued. However, language barriers often hinder learners' comprehension and appreciation of such texts. To address this challenge, our research endeavors to construct an algorithm that leverages machine translation techniques to facilitate the reading process for English learners. By integrating advanced translation algorithms and natural language processing methods, the proposed system aims to provide accurate and contextually relevant translations of foreign literature into English. Additionally, the algorithm will incorporate features tailored to the needs of English learners, such as vocabulary assistance, grammatical explanations, and cultural context annotations, to enhance their reading experience and comprehension levels. Through rigorous experimentation and evaluation, the effectiveness and usability of the algorithm will be assessed, with the ultimate goal of empowering English learners to engage more deeply with foreign literature and broaden their linguistic and cultural horizons.

Using Cross-Cultural Machine Translation Technology to Promote Communication and Cooperation in English Courses

In an increasingly globalized world, effective cross-cultural communication and cooperation are paramount skills for individuals across various domains. This research paper investigates the integration of machine translation technology into English language courses as a means to enhance cross-cultural communication skills and foster cooperation among diverse linguistic communities. The study explores the efficacy of machine translation tools in facilitating language comprehension, expression, and interaction among learners from different cultural backgrounds. Drawing on both theoretical frameworks and empirical evidence, the paper examines the impact of machine translation on language learning outcomes, intercultural competence development, and collaborative learning experiences. Additionally, the research evaluates the perceptions, attitudes, and experiences of students and instructors towards the integration of machine translation technology in English language education. Through a combination of qualitative and quantitative analyses, this study contributes to the understanding of how technology can be leveraged to bridge linguistic and cultural divides in educational settings. The findings offer valuable insights into the potential benefits and challenges associated with the use of machine translation technology for promoting cross-cultural communication and cooperation in English courses, with implications for curriculum design, pedagogical practices, and future research in language education and technology-enhanced learning..

Ainu–Japanese Bi-directional Neural Machine Translation: A Step Towards Linguistic Preservation of Ainu, An Under-Resourced Indigenous Language in Japan

This study presents a groundbreaking approach to preserving the Ainu language, recognized as critically endangered by UNESCO, by developing a bi-directional neural machine translation (MT) system between Ainu and Japanese. Utilizing the Marian MT framework, known for its effectiveness with resource-scarce languages, the research aims to overcome the linguistic complexities inherent in Ainu's polysynthetic structure. The paper delineates a comprehensive methodology encompassing data collection from diverse Ainu text sources, meticulous preprocessing, and the deployment of neural MT models, culminating in the achievement of significant SacreBLEU scores that underscore the models' translation accuracy. The findings illustrate the potential of advanced MT technology to facilitate linguistic preservation and educational endeavors, advocating for integrating such technologies in safeguarding endangered languages. This research not only underscores the critical role of MT in bridging language divides but also sets a precedent for employing computational linguistics to preserve cultural and linguistic heritage.

Realization of Chinese-English Bilingual Speech Dialogue System using Machine Translation Technology

The realization of a Chinese-English bilingual speech dialogue system through machine translation technology involves developing a sophisticated system capable of seamlessly translating spoken language between Chinese and English in real-time. This system employs cutting-edge machine learning algorithms, neural networks, and natural language processing techniques to accurately interpret and translate speech inputs from one language to another. By integrating advanced speech recognition and translation models, users can engage in fluid and natural conversations across language barriers, opening up new possibilities for cross-cultural communication and interaction. This paper introduces a Statistical Phase-based Bilingual Speech (SPBS) system designed to facilitate seamless language translation and dialogue between multiple languages, with a focus on Chinese and English. Leveraging advanced machine learning models and techniques, such as Recurrent Neural Networks (RNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) architecture, the SPBS system achieves high translation accuracy, computational efficiency, and fluency of translations. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB. Furthermore, the paper presents the machine learning settings and training progress of the SPBS system, demonstrating its effectiveness in accurately classifying and translating speech inputs across languages. The system's multilingual model attains an impressive translation accuracy of 97% while processing 10 sentences per second, with positive feedback on the fluency of translations. Trained on a substantial dataset of 1 million bilingual sentence pairs, the SPBS model maintains a compact size of 500 MB.

Machine translation as a form of feedback on L2 writing

Abstract With advances in artificial intelligence (AI), many language teachers have started exploring the classroom implications of AI-powered technology, including machine translation (MT). To examine the usefulness of MT technology in writing instruction, we conducted a mixed-methods study comparing two types of written feedback: comprehensive direct Teacher Corrective Feedback (TCF), and MT feedback. Participants were 23 Japanese university students in an intact L2 writing classroom. Sample size adequacy was confirmed through a priori power analysis. Participants were instructed to describe a picture prompt in L2 English and then in L1 Japanese. Half the participants received first TCF then MT on their L2 English text, while the order was reversed for the other half. Participants in both conditions were then asked to study the feedback and describe the same picture prompt without the feedback. In the following phase, both groups completed the same tasks in reverse order. Participants also responded to a survey exploring their engagement with the feedback. Results reveal that: 1) TCF improved complexity; 2) MT improved accuracy and fluency; and 3) variation in outcomes may be explained by the different ways in which participants engaged with both TCF and MT. Implications for appropriate classroom use of MT are discussed.

The Integration of Machine Translation Technology in the Realm of Legal Interpretation

This article aims to provide an overview of the application of machine translation technology in the field of legal translation, exploring its potential, challenges, and future directions. Firstly, it reviews the development process of machine translation technology, including rule-based methods, statistical machine translation, and the application of deep learning methods. Secondly, on the basis of in-depth deconstruction of the operating rules of the big language model, it is demonstrated that the highly modeled written legal language is highly consistent with the underlying logic of the big language model because of its standardization, accuracy and de-contextualization, so compared with other styles, machine translation technology will achieve better translation results in the field of legal translation. In addition, multimodal technology can also be applied to court interpretation, which greatly alleviates the shortage of qualified interpreters and maintains judicial justice. Furthermore, it discusses the human-machine collaborative model in legal translation, emphasizing the importance of human proofreading and review in ensuring translation accuracy and reliability. Lastly, it summarizes the prospects and challenges of machine translation technology in the field of legal translation. Through a systematic review and analysis of relevant literature, this article reveals the immense potential of machine translation technology in legal translation. It can significantly enhance the efficiency and quality of legal translation, thereby enhancing the capacity of legal language services.

History of the development of machine translation systems (from the early stages to the beginning of the 21st century)

The article examines certain periods of machine translation development and the methods used to create various machine translation systems. To ensure correct historical periodization, the author defines the criteria for classifying the development of machine translation. Special attention is paid to the development of individual machine translation systems, as well as the events that had a significant impact on both the development and the hindrance of progress in the field of machine translation. Additionally, the author considers the ALPAC report and its subsequent “quiet period” of machine translation development, analyses the key specialists who have been involved in the development and improvement of machine translation systems and machine translation technologies, categorizing them into four large groups. The article reviews the dominant basis of machine translation research until the late 1980s, rooted in various linguistic rules such as parsing rules, lexical rules, lexical transfer rules, syntactic generation rules, morphology rules, etc.The author traces the emergence of the corpus-based method in machine translation, as well as its further expansion through experiments in computational modelling of cognition and perception, including the study of parallel computing, neural networks and connectionism, which offered the prospect of making the systems “learn” from past successes and failures. Besides, the author also examines the emergence of the interlingualapproach for multilingual translation of technical manuals and the history of the “pivot language”, along with the use of SLLP (specialized languages for linguistic programming).

Evaluation of domain sulfur industry for DIA translator using bilingual evaluation understudy method

Evaluation is important part of our system development cycle; it also contributes to improving new machine translation (MT) technology optimum via comparing them with the traditional systems available to determine the weaknesses and the effectiveness to be improved in the proposed MT system. This work aiming to make a study that evaluate the performance and effectivness of the domain sulfur industry (DSI) for English-Arabic DIA translator quality. The recent study has conducted evaluating by making a comparison between this programme with the prominent Google translator through applying a rendering of 1,200 English sentences in bilingual evaluation understudy (BLUE) method. The obtain results show that the efficiency of Google translator is about 30.325%, while DIA translator efficiency in domain sulfur industry is about 73.325% and it’s more effective and give a better translation accuracy. The BLUE method efficiency is about (90.478%) compared with the human expert evaluator.

Unveiling the New Frontier: ChatGPT-3 Powered Translation for Arabic-English Language Pairs

This study evaluates the aptitude of ChatGPT for Arabic-English machine translation. The main objective of this research is to scrutinize the quality of ChatGPT's translations and compare its performance against machine translation systems, such as Google Translate, which are intricately tailored for translation purposes. In addition, the study seeks to investigate the potential integration of ChatGPT into translation workflows. Furthermore, we aspire to discern whether ChatGPT's translation efficacy harmonizes with or diverges from the profound finesse exhibited by human translation expertise. To accomplish this, a comparable corpus of 1000 English sentences and their corresponding Arabic translations was employed to evaluate the translation outputs of both machine translation systems alongside a human translation reference. The corpus was sourced from Tatoeba, an open online platform and underwent electronic assessment using the BLEU (Bilingual Evaluation Understudy) metric. The results indicate a marginal advantage of ChatGPT over Google Translate in delivering high-quality translations. Upon evaluating the corpus, we ascertain that ChatGPT performs impressively well compared to specialized translation systems like Google Translate. However, despite these promising findings, it is essential to acknowledge that even the most advanced machine translation technology, ChatGPT, cannot currently match the proficiency of human translation, at least not in the near future.

GPT를 활용한 중-한 문학번역에 대한 고찰

This study investigates the use of advanced machine translation technologies like GPT in literary translation. It highlights the importance of conveying an author's intent and cultural nuances, a task traditionally reliant on human translators. The paper focuses on translating idioms from the Chinese novel “To Live(活著)” using GPT and traditional machine translation (MT), comparing their outputs. Findings indicate that GPT, through user interaction, offers a nuanced understanding of context and idioms, showcasing its effectiveness in handling complex expressions. The paper underscores the importance of appropriate prompt setting in AI translation, suggesting future research at the nexus of technology and literary expression. It concludes that the quest for more precise and nuanced translations will advance through a synergistic relationship between AI and human translators.

Evaluating machine translation of literature through rhetorical analysis

This paper looks at how well ChatGPT and DeepL, two AI tools, translate literary works. Not only can ChatGPT translate text, but it can also carry out other jobs. DeepL is a service that performs computer translation and uses neural networks. The paper looks at how ChatGPT and DeepL translate books, poems, and dialogues compared to translations done by humans. The paper also talks about the pros and cons of using machine translation for literary reasons, including issues of creativity, style, and adapting to different cultures. The paper uses both new and old studies on machine translation technologies and how they work with human translation. The paper comes to the conclusion that ChatGPT and DeepL are useful but imperfect tools for translating literature, and they require human review and improvement. The paper adds to the fields of machine translation and natural language processing by looking at how two cutting-edge AI tools, ChatGPT and DeepL, can be used to translate literary works. The paper also adds to literature studies and digital humanities by looking into what machine translation can and can't do for creative writing and dialog systems. The goal of the paper is to encourage researchers, translators, writers, and users from different fields to work together and talk to each other. ⁤

A Comparative Analysis of Human Translation and Machine Translation's Impact on Pedagogy from a Linguistic Perspective

In this rapid growing use of advanced technology, translation industry is going through extensive and profound changes, in order to cater the needs of emerging translation service, more people are likely to use machine to help them translate to increase efficiency. Undoubtedly, translation, which used to be human-oriented, has gone through transformation and upgrading both in its form and content. However, along with its benefits, machine translation also presents certain challenges. One such concern is the lack of cultural sensitivity in the translations it produces. Additionally, as languages continuously evolve with the introduction of new words and expressions, machine translation systems may struggle to keep up, leading to outdated translations. These issues and others necessitate proactive solutions to improve machine translation technology. This paper aims to address these problems by conducting a thorough literature review and performing case analyses from the perspectives of linguistics and pedagogy. This study reveals that translators equipped with the proficiency to leverage machine tools while possessing a comprehensive grasp of diverse translation skills and knowledge can thrive in the future. Similarly, educators can judiciously incorporate machine translation as a supplementary tool in teaching, empowering students to enhance their translation skills and capabilities.

The Characteristics of Face-to-Face Communication Using Consecutive Speech Machine Translation Technology

The growing role of technology in society, particularly machine translation, has grown due to its enhanced accuracy. Yet, communication quality depends not only on translation precision but also on user factors. It is vital to understand communication characteristics when using machine translation for its future application in daily life, business, and education. This study investigates these characteristics in face-to-face communication using consecutive speech machine translation technology. Participants were international students at a Japanese university who conversed using speech machine translation. Despite mistranslations, they continued to communicate, either understanding most of the message or giving up due to incomprehension. Notably, the turn-taking order was alternated mechanically, even when there was a long wait for the other person to speak. Unlike previous studies, gestures were not used to overcome communication barriers due to mistranslations; rephrasing was preferred. This study proposes the concept of “speech production load,” the burden of device operation and translation waiting time, as a determi-nant of the relationship between a speech machine translation system and user communication behavior in the context of modern technology.

Exploring the Role of Machine Translation in Translating English Collocations into Arabic: Insights from Student Translators

Machine Translation (MT) has increasingly become an essential technology in the modern age. MT technology is currently used by many EFL learners as a learning facilitator. They are using MT as an essential tool to assist them in their foreign language learning activities. Several studies have focused on investigating the EFL students’ use of and attitudes towards MT in various EFL learning activities including reading, writing and vocabulary acquisition. However, few studies have been conducted on exploring the EFL learners’ use of MT technology in the translation of collocations, especially in the Arabic context. This study addresses this gap by investigating the impact of MT on the translation of English lexical collocations into Arabic. It presents a corpus of twenty English collocations given to thirty third-year translation students at an Omani university, who utilized an online MT system for their translations. Employing a descriptive, qualitative approach, the study assesses students' strategies and the accuracy of MT-generated equivalents, drawing from translation models by Vinay and Darbelnet (1958) and Newmark (1988). The results indicated that the students were able to generate correct translations for certain collocations when using MT, but there were inaccuracies in the translation of other collocations. The study emphasized the importance of not solely depending on MT because doing so might reduce students' willingness to actively search for the most appropriate translations on their own. This suggests that a balanced approach to using MT and encouraging students to develop their translation skills independently is advisable. Future research can explore the use of Machine Translation in translating collocations in languages beyond Arabic and within different cultural and linguistic contexts.

EVALUATION OF THE ADEQUACY OF WRITTEN SPECIALIZED TEXTS MACHINE TRANSLATION

Machine translation has become critical for overcoming language differences in the age of global communication, especially in specialized fields. This article discusses the complex process of evaluating the adequacy of machine translation for written specialized literature. The study analyzes the mistakes made by Google Translate (GT) and DeepL Translate (DT) when translating biology textbooks. The paper emphasizes that specialized texts require not only terminological accuracy but also context preservation, which makes machine translation in this field extremely challenging. Several evaluation approaches have been considered in previous work, emphasizing the need for human editing alongside machine translation systems. Machine translation errors, ranging from terminological and semantic mistakes to stylistic and grammatical errors, emphasize the importance of thorough post-editing. The results of the study showed that despite the correct translation of commonly used terminology, its use in complex sentences caused difficulties. Both systems often failed to recognize details, leading to distorted interpretations. Stylistic mistakes, such as word-for-word translation disregarding context, further weakened the translated sentences. The translation was also accompanied by grammatical errors that demonstrate the complexity of syntactic structures in specialized topics. The study revealed minimal differences between GT and DT, with GT translating terminology better and DT demonstrating more efficient grammatical structures. However, both systems used word-for-word translation, which resulted in stylistic errors and inaccurate word choice. This paper emphasizes the need for human editing to ensure the correct translation of specialized materials, as well as the need for continuous improvement of machine translation technology to ensure the accuracy of terminology and preserve context in specialized texts.

Neural machine translation in AVT teaching in China: An in-depth analysis from the readability perspective

As audiovisual translation (AVT) becomes more complex and diverse, the need for advanced machine learning techniques has been increasing sharply, driving the widespread adoption of neural machine translation (NMT) technology in the field. This study contributes to the literature by evaluating the performance of NMT technology in AVT teaching. Based on readability theory, we constructed an evaluation framework with 12 indicators, built comparable corpora consisting of human and post-edited subtitle translations of corporate videos, and used them to examine the performance of four online NMT systems (Google Translate, Baidu Translate, Bing Translator, and Youdao Translate) in AVT teaching. Our statistical analyses and case studies show that Google Translate outperforms the other three platforms in all the readability tests, and it can enhance the readability of post-edited subtitles at five levels (word, syntax, textbase, situation model, genre and rhetorical). The performance of the other three platforms varies across different tests. Concrete examples are provided to substantiate the statistical analyses. Our study adds value to existing research both by examining the application and performance of NMT in AVT teaching and by suggesting potential directions for the refinement of current NMT systems.

외국어 학습 도구로서의 기계 번역

Objectives The objective of this study was to examine the perceptions and attitudes of undergraduate French language majors about neural machine translation through a survey.
 Methods The study involved students enrolled in courses such as “French Literature and Translation” and “Advanced French Composition 2” at a university in Seoul. Over the course of a semester, students actively participated in translation and composition exercises aided by machine translation technology and draw up Integrated Problem and Decision Reporting (IPDR) detailing the use of machine translation for problem-solving in the translation process. Subsequently, a survey was conducted to assess the participants' perceptions and attitudes toward machine translation.
 Results The participants exhibited highly positive responses towards the effectiveness and convenience of using machine translation for learning, highlighting benefits such as time-saving and enhanced learning outcomes. However, the participants displayed a flexible attitude toward the machine-generated results and modified them instead of accepting unconditionally. While statistical analysis did not reveal significant differences in perception and attitude based on enrolled courses or grades, open-ended responses indicated that high-achieving students emphasized the learning benefits and effectiveness of machine translation, whereas lower-achieving students focused on its convenience, particularly in terms of time-saving.
 Conclusions With the increasing integration of machine translation into foreign language education, understanding how French language majors perceive machine translation can aid educators in devising teaching methods and learning strategies that optimize efficient and effective learning outcomes. This study contributes to the exploration of using machine translation as a tool to enhance language learning.