Machine Translation Tools Research Articles

Predicting attitude, use, and future intentions with translation websites through the TAM framework: a multicultural study among Saudi and South Korean language learners

Machine translation (MT) practice and activity development in education are possible when students with diverse backgrounds contribute to helping define how MT can best be used for language learning. This study employed a questionnaire based on an adapted version of the technology acceptance model (TAM) to gain perspective on the perceptions, attitudes, and use of MT tools like Google Translate among Saudi and Korean university students learning English as a foreign language (EFL). A total of 470 students were recruited from Saudi Arabia and South Korea to complete a questionnaire measuring scales on perceived ease of use, perceived usefulness, attitude, actual use, and intended future use of MT websites. Structural equation modeling analyzed the relationships among the TAM variables. Results found that Saudi and Korean students both reported high levels on the TAM variables, indicating that translation tools were easy to use and useful for studying language. Lower L2 proficiency correlated with greater perceived usefulness and a more positive attitude. Students frequently use MT tools when learning English and plan to continue using them in the future. The model explained 71% of actual use and 42% of behavioral intention to use MT for language learning. Furthermore, Saudi students reported a slightly easier time using MT tools for language learning, while Korean students planned to use MT more often. Other findings, along with practical applications, are presented in this paper.

Linguistic reasons and professional concerns behind Turkish academicians' self-translation of academic texts

This study analyzes the linguistic reasons and professional concerns that motivate Turkish academicians to self-translation. To this end, a structured interview was conducted with twelve academicians from three disciplines who reported that they self-translate. The results indicate that the main linguistic reason for academicians to resort to self-translation is feeling incompetent in writing directly in English. The most common professional concern is to contribute to international literature and to get academic appointments and academic incentive payment points by publishing in English. It has been observed that academicians have created a system of publishing in English through self-translation as a solution to these two groups of reasons and concerns: they first write their articles in Turkish, then self-translate with machine translation tools, and have them proofread before publishing. The results offer insights into the self-translation of academic texts through Turkish academicians; however, further comparative research with other non-native English-speaking academicians is needed.

What Are the English to Spanish Translation Methods Used on Written Health-Related Information?

Objective: This systematic review was conducted to examine methods and tools used to translate written health-related information from English to Spanish. Methods: The design of this study was a systematic review. This review search was of all published articles up to July 2022. Three databases were used: CINAHL Plus, MEDLINE, and PubMed. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis flowchart was used in the systematic review. Results: A total of 17 articles were used in the analysis. Six (35.3%) of the articles used a machine and human translation method, and eight (47%) used Google Translate as the machine translation tool. The screening, full-text review, and data extraction were done using the Covidence software. Discussions: As translating health-related information accurately is crucial, using metrics or standardizing one is urgently needed. Nursing implications: This review highlights that in addition to a lack of standardized metrics, there currently is no criterion on the qualifications or credentials required by individuals who translate written information from English to Spanish, allowing anyone to do so. This is concerning because it is critical that Spanish speakers and those with limited English proficiency receive linguistically accurate and appropriate resources that allow them to improve their health literacy and promote health.

Nursing students' attitude and perception toward machine translation for learning English medical terminologies

Nursing students must learn English medical terminologies since they must read and follow them to deliver patient care. Arab students highly use machine translation (MT) to understand those terminologies. Therefore, this study aimed to reveal the attitude and perception towards MT for learning English medical terminologies among undergraduate nursing students in Saudi Arabia. This study used an exploratory study design and comprised all final-year nursing students of the undergraduate nursing program in Abha (n=80) and Muhayil (n=80) female campuses of King Khalid University (KKU), Saudi Arabia. Those students (N=160) were administered a self-structured online questionnaire, and 132 responded to the questionnaire. More than 95% agreed that MT tools were easy to use, fast, and saved time. Though, 81.1% required more training in using MT tools. Furthermore, 97.7% perceived that MT helped them learn English medical terminologies. More than 90% reported that MT helped them understand and memorize English medical terminologies. Most nursing students stated that MT boosted their confidence (94.7%) and improved the quality of learning (96.2%) concerning English medical terminologies. Nursing students of KKU presented a positive attitude and perception toward MT for learning English medical terminologies. Saudi nursing schools should provide adequate training in using MT tools to enhance their students' learning of English medical terminologies, which would help them in clinical practice.

Translating Research into Practice: Plain Language and Writing for Machine Translation Guidelines

ABSTRACTScholars seek to translate research into practice through science communication, but the overwhelming use of English makes it challenging for some local community actors to access research findings. Machine translation (MT) tools can help, but the underlying data‐driven approach does not work equally well for all languages and domains. If plain language summaries can be rendered more MT‐friendly, this could make it easier for speakers of other languages to access research. This poster compares guidelines for plain language and guidelines for writing for MT to determine their compatibility and potential usefulness for creating reader‐ and MT‐friendly summaries.

On the challenges of legal translation

This short commentary focuses on language and law and legal translation. It stresses the importance of language used in legal texts in the legal context and the complexities when more than one language is involved in interlingual and cross-cultural communication in law. There are age old challenges in legal translation as well as new ones in the digital age with the increasing use of machine translation systems. It is imperative today that human legal translators are familiar with machine translation tools and aware of how computer aided translation technologies process information and their strengths and weaknesses. To be able to effectively use machine translation systems should become a compulsory part of the digital literacy and skill sets of legal translators in the twenty-first century.

Adapting an English Corpus and a Question Answering System for Slovene

Developing effective question answering (QA) models for less-resourced languages like Slovene is challenging due to the lack of proper training data. Modern machine translation tools can address this issue, but this presents another challenge: the given answers must be found in their exact form within the given context since the model is trained to locate answers and not generate them. To address this challenge, we propose a method that embeds the answers within the context before translation and evaluate its effectiveness on the SQuAD 2.0 dataset translated using both eTranslation and Google Cloud translator. The results show that by employing our method we can reduce the rate at which answers were not found in the context from 56% to 7%. We then assess the translated datasets using various transformer-based QA models, examining the differences between the datasets and model configurations. To ensure that our models produce realistic results, we test them on a small subset of the original data that was human-translated. The results indicate that the primary advantages of using machine-translated data lie in refining smaller multilingual and monolingual models. For instance, the multilingual CroSloEngual BERT model fine-tuned and tested on Slovene data achieved nearly equivalent performance to one fine-tuned and tested on English data, with 70.2% and 73.3% questions answered, respectively. While larger models, such as RemBERT, achieved comparable results, correctly answering questions in 77.9% of cases when fine-tuned and tested on Slovene compared to 81.1% on English, fine-tuning with English and testing with Slovene data also yielded similar performance.

Artificial Intelligence in Education

Despite its controversial nature, machine translation (MT) has been increasingly integrated into learning in the past decade. This controversy arises from two different beliefs. While some believe that MT negatively impacts students' language proficiency, others argue that it allows students to stay abreast of technological advancements. Despite the numerous risks associated with the unstoppable development and irresistible use of MT, it is imperative to explore appropriate integration methods instead of outrightly banning its use in learning the translation practice. Consequently, this article examines existing research on methods of using MT in classroom learning and highlights its strengths and limitations. The article explores pedagogical solutions to harness the capabilities of MT and proposes a novel approach for the practical and efficient utilization of GNMT in translation-learning classroom. The findings propose a novel strategy for optimizing the efficacy of GNMT tools in the context of classroom learning. Also, they emphasize the importance of integrating MT tools in classroom and to the curriculum design as a fast-developed technology tool.

Thoughts on Growth Mindset in Language Education

The world today is facing trying times with democracy in crisis around the world, extreme weather events, and state fragility coming on the heels of a global pandemic. However, crises have been the driving force in the evolution of human society. The problems confronting us today cannot be resolved by one organization or one country alone. What is needed is effective communication among peoples, groups, and countries. Even with technological advances in machine translation and AI tools, we as language teachers have much that we can do. This talk will suggest the importance of building social capital through communication, present evidence-based education concepts for efficient teaching, and examine how to work with Gen Z students. Education for a VUCA world of volatility, uncertainty, complexity and ambiguity (Bennett & Lemoine, 2014) must prepare students to learn how to take charge of their own learning experiences and thus develop a growth mindset to embark on a never-ending life-long adventure of learning. They can thus contribute to social capital and a better human society.

Machine translation in language acquisition: A study on EFL students' perceptions and practices in Saudi Arabia and South Korea

AbstractBackgroundLanguage learners are taking advantage of the increased accuracy afforded by machine translation (MT) tools like Google Translate. There is a growing debate as to whether these tools support or hinder English as a foreign language (EFL) education. Some EFL students are drawn to MT tools to save time and energy when communicating, but how this use relates to second language acquisition needs to be clarified.ObjectiveThis study investigated the perceptions and practices of MT tools among university EFL students in Saudi Arabia and South Korea.MethodsThe study surveyed Saudi (n = 310) and South Korean (n = 160) university students to explore the use of MT tools in EFL learning. It used mean scores, correlation analysis, and structural equation modelling to understand the perceived benefits and limitations of MT tools and their influence on the use of translation websites. The findings were triangulated using content analysis of open‐ended items.Results and ConclusionFindings indicate students showed a high level of acceptance and utilization of MT tools. English avoidance behaviour (B = 0.43), benefits (B = 0.32), and foreign language proficiency (B = −0.19) predicted 61% of actual use of MT tools like Google Translate. Further, students frequently used MT tools despite the reported concerns about accuracy mistakes. Saudi students listed a more comprehensive array of why they use MT tools than Korean students, while Saudis and Koreans frequently used MT tools for individual word and sentence translations.ImplicationsStudents should edit MT output when writing and are encouraged to use the voice recording and other functions of MT tools to learn language skills. Instructors must prevent overuse and dependency on MT tools, especially among low‐proficiency students who want to avoid using English.

Machine translation and culture-bound texts in translator education: a pilot study

ABSTRACT Mass adoption of neural machine translation (NMT) tools in the translation workflow has exerted a significant impact on the language services industry over the last decade. There are claims that with the advent of NMT, automated translation has reached human parity for translating news (see, e.g. Popel et al. 2020). Moreover, some machine translation (MT) research has already been done in the context of literary texts. In this paper, we share the results of a pilot study carried out with two groups (a pre-course group and post-course group) of MA-level students participating in a course that involved translating culture-bound texts. The students’ role was to post-edit and evaluate two machine-translated stories (Polish legends), marking their comprehensibility and accuracy. We discuss the lessons learnt during this pilot study, the critical errors detected by the students and their perceptions of the end products and the experiment itself. We report noticeable differences found between the pre-course group and the post-course group in terms of language awareness and the speed and quality of their post-editing (PE) performance. Our results also show that the task of post-editing culture-bound texts offers students a unique and enjoyable setting, enabling them to assess translation technology and hone their translation skills at the same time.

Looks like google to me: Instructor ability to detect machine translation in L2 Spanish writing

AbstractThis article reports the results of an empirical study designed to determine the degree to which college instructors of Spanish can distinguish between machine translation (MT) and non‐MT writing samples produced by second language (L2) learners of Spanish in an intermediate‐level writing course. We also investigated relationships between detection accuracy rates, instructor teaching experience, and text type (narrative or argumentative), as well as signs instructors consider indicators of both kinds of writing. Results demonstrated that instructors were able to distinguish MT from non‐MT writing with a high degree of accuracy by relying on a broad array of indicators. However, neither text type difference nor instructor experience related significantly to detection ability. These findings have practical implications for the L2 classroom with regard to instructor response to MT use and the integration of MT tools to support L2 writing development.

Resolving Lexical Verb Ambiguity in Yoruba through Semantic Web Annotations

Lexical verb ambiguity refers to the potential for multiple interpretations of units of speech in languages that renders it difficult or impossible to understand a lexical item without additional information. The goal of semantic web research is to allow the vast range of web-accessible information and services to be more effectively exploited by both humans and automated tools. Technological advancement has given rise to scholars developing automated translation systems from English to the Yorùbá language. However, the problem arises that these tools are not coping easily with semantic nuances found especially in Yorùbá verbs. To explore this problem, a number of Yorùbá lexical ambiguous verbs which may pose serious challenges to the existing machine translation tools were generated and analyzed. To facilitate a solution to the problem, the identified verbs are annotated following the Qualia Structure (QS) of the Generative Lexicon. Protégé 4.5 is employed as the editing tool to map the semantic interpretations of the QS to the semantic web. With the aid of the unique formal specification implemented for each of the lexically ambiguous verbs, the existing automated translation system can now utilize the Internationalized Resource Identifier (IRI) of the Unified Resource Locator (URL) found in the model developed to link the loop and thereby alleviate the problem of ambiguity for the automated translation system. This paper, therefore recommends that more efforts be geared towards ontological annotation in order to foster more automated tools that will be built for the Yorùbá language.

대학 영문독해 수업에 기계번역(post-editing)의 활용 및 오류분석

This paper aims to explore the possibility of using machine translation in English reading classes. For this purpose, 38 subjects were divided into two groups according to the TOEIC score and required to translate and submit a 386-word English text through post-editing translation after using a machine translation tool. This analysis shows the four major error types of contextual comprehension, sentence structures, pronouns, title nouns. In addition, the subjects were required to participate in the questionnaire survey related to the effectiveness of reading comprehension classes using machine translation. The research also proved how effective English reading classes using machine translation were in comparison with English grammar-based reading classes. We can find the similarities between machine translation and students’ post-editing translation in error ratio and characteristics, and the differences between the two English levels in terms of post-editing correction methods and the number of translation errors. This paper is meaningful in that this study will contribute to diversification of teaching methods and improvement of the teaching quality in English reading classes by employing machine translation in English reading classes.

Comparing Digital And Manual Translation Proficiencies In English Language Education: An Analysis Of Translation Quality

In this study, we aim to compare the quality of digital and manual translation in English texts for the English Language Education Program. Understanding the strengths and weaknesses of each translation method can help educators improve their teaching strategies and provide better learning outcomes for students. Thus, the research will be guided t the student using in translation between digital and manual translation in English texts for the English Language Education program and the quality of digital and manual translation. , digital translation tools may not capture the nuances and cultural aspects of language that are essential in language learning and communication (Zarei & Gholami, 2020). Manual translation provides a more accurate and nuanced translation than digital tools, as it takes into account context and cultural nuances (Ferraresi & Bernardini, 2019). This study employs a qualitative approach with a comparative method between two translation techniques digital and manual. Data is collected from an abstract of an article with “Pentingnya Pendidikan Bahasa Inggris di Sekolah Menengah” with the original text Bahasa Indonesia then translate it to English language using digital and manual translation. In general, however, studies comparing digital (machine) translation and manual (human) translation in the field of language education have found that while machine translation tools like Google Translate can be helpful for quick and basic translations, they often lack accuracy and precision, particularly in complex or nuanced language tasks.

Translation under the guidance of translation variation technique: the case of written advertising

ABSTRACT This paper sought to study the strategies of variation in translating advertisements from English to Chinese and vice versa. For this, an analysis of a sample of 28 advertisements, logos, and headlines was conducted, among which 8 samples having English-Chinese and Chinese-English variation translation alternatives were selected for further examination. Their discourse analysis showed that the average number of possible variation translations per material unit (message, headline, logo) was 1–2, whereas for literal translation, only 1 variant was available. The success of variation translation was achieved through grammatical transformation, omission, approximation, addition, lexical units’ relocation and substitution, synonymisation, and paraphrasing. The comparative analysis showed that the literal translation was less effective than the variation, as it was not always possible to achieve the maximum effect from the advertising and influence the reader. The collected findings may add to further research in the field of linguistics, translation, marketing, and copywriting, as they provide useful data on the linguistic strategies of modern brand advertising campaigns. They could also be used in the subsequent studies focusing on machine or computer-assisted translation tools or evaluating the quality of translation and its impact on consumers.

Exploring the Process and Strategies of Chinese–English Abstract Writing Using Machine Translation Tools

The present study explores English as a foreign language (EFL) learners’ processes and strategies when using machine translation (MT) tools in academic abstract writing. Eight EFL graduate students were introduced to translation-friendly writing strategies using Google Translate and were required to produce an English abstract with the aid of a machine translation tool. The study used qualitative and quantitative approaches in data collection and analysis. A triangulation process was developed and implemented, including think-aloud protocols during the writing session, surveys, and individual interviews after the writing session. The findings suggested that the translation-friendly writing strategies introduced to the participants were useful in enhancing the quality of their writing. Each participant demonstrated individual strategic uses of MT. Among the various strategies reported, back translation was the most commonly adopted one; that is, they first composed an abstract in Chinese (L1) and engaged in multiple rounds of translation between Chinese and English using MT; when problems were identified in the English abstract, they modified the Chinese abstract using translation-friendly writing strategies to enhance the quality of MT translation output. Most of the translation problems identified by the participants were related to non-academic expressions. While participants were satisfied with the quality of the abstracts produced with the aid of MT, they raised ethical concerns regarding the use of MT in academic writing. These findings suggest that MT has fundamentally changed the process of academic writing in English and call for the re-examination of the purpose of academic writing instruction and the approaches employed.

Tulevaisuuden kääntäjän kuva

Commercial specialized translation is a rapidly evolving industry most prominently characterised by pressure towards lower prices and the implementation of new technological tools. Based on interviews of translation professionals, I describe how they expect the industry to develop: which positive and negative trends they recognize, which competence profiles translators will need, and how the implementation of machine translation tools and features will change their work. It is evident that while translation professionals are worried about the negative price development and have somewhat mixed feelings about machine translation, they believe that human translators will continue to be needed for the foreseeable future. Anticipated changes in competence profiles include increased specialization, the ability to work fast, and the ability to adopt new tools. The interviewees also expect that translators may be motivated to specialize either in post-editing of machine-translated texts, or in highly creative translations. They are well aware of the benefits and risks of machine translation; while recognizing the considerable improvement in machine translation technology, they do not believe that the typical quality problems of neural machine translation can be fully resolved.

When Translation Goes Digital: Case Studies and Critical Reflections. Renée Desjardins, Claire Larsonneur, and Philippe Lacour

The emerging and growing field of ‘Digital Humanities’ (DH) has brought profound and comprehensive changes. Particular concern has been aroused by the impact of advanced technologies on translation and Translation Studies (TS). Cutting-edge technologies have developed effective online machine translation (MT) tools such as Google Translate and DeepL. Consequently, there has been an on-going debate about whether digital technologies will completely replace human translation in the near future, which seems unlikely to me. When Translation Goes Digital: Case Studies and Critical Reflections (Desjardins et al., 2021) supports this position and bolsters my confidence in human translation. Consisting of ten studies presented at the sixth International Association for Translation at Hong Kong Baptist University, the book is structured in three parts. Aiming to ‘explore how the digital landscape was impacting translation and TS, broadly and specifically’ (1), it is suitable for scholars, translators, or any translation practitioners curious about the latest studies regarding TS and the current digital world, the relationship between human translation and digital technologies, and how scholars define the relationship between DH and TS.

Saudi Translation Agencies and Translation Centers: A Study of Post-Editing Practices

This qualitative research aims to investigate and analyze the practice of post-editing activity in Saudi translation agencies and translation centers. The study’s focus is on the importance of post-editing Machine Translation (MT) output as an approach to upgrade the quality of MT results to enhance the accuracy of translations. The study data were collected through an electronic survey (questionnaire) comprising short questions about post-editing practice and guidelines in addition to their knowledge about post-editing certification programs. The participants in this study were 18 professional translators working in certified translation agencies and translation centers across different universities between Jeddah, Dammam, and Riyadh cities. The results of the study showed that the participants post-edited the MT outputs in their tasks and recognized the importance of being aware of post-editing standards and guidelines. Further, the findings demonstrated that the majority of the participants used machine translation tools. Finally, they were positively disposed about post-editing certification programs to take employment as post-editors. Based on the results of this study, it is recommended to analyze the state of post-editing in Saudi Arabia on a larger sample to investigate the attitudes of Saudi translation students towards practicing post-editing of MT outputs, and analyzing the activity of post-editing MT in specialized texts by Saudi professional translators.