Students’ Common Mistakes in Technical Texts When Using Computer Technologies

The integration of machine translation (MT) and artificial intelligence (AI) has significantly enhanced the precision of MT systems producing translations that rival the quality of skilled human translators. This innovation has broadened the applicability of AI-enabled MT systems attracting users for a variety of purposes. This paper explores the following areas: the application of AI-enabled MT, specifically Google Translate (GT) in English as a Foreign Language (EFL) classroom. Empirical findings and recent research indicate that students are increasingly reliant on MT particularly in higher educational settings where English is not their first language. The purpose of this research is to provide insight on the practices, beliefs and goals of EFL learners who use GT in the classroom. Data was gathered from 234 university students through a questionnaire along with pre- and post-tests to compare the quality of writing drafts created with and without the aid of GT. The findings are consistent with those of previous research conducted worldwide highlighting the immense potential of AI-enabled MT in not only boosting students' learning experiences but also cultivating more independent learners. The study also indicates that students have positive attitudes towards GT and employ diverse search strategies to address various language-related challenges.

The progress of Artificial Intelligence brings about profound developments in Computer Aided Translation (CAT). Therefore, many language service providers use CAT tools to improve translation efficiency while ensuring quality. With people's attention to health deepening, popular medical texts are becoming mainstream. This article studies the translation of medical texts in the mode of Machine Translation and Post-editing (MTPE). The source text This Is Your Brain on Food is pre-translated by Google Translate and post-edited by the translator. Based on this practice, the Machine Translation (MT) errors are summarized, and Post-editing (PE) solutions are illustrated at the lexical level. There are MT errors about mistranslation and omission. Several PE solutions are presented in the article, which cover replacement, supplementation and conversion of part of speech. It is shown that the MTPE mode can improve the efficiency of medical translation. Post-editing can ensure the translation effect as equal as possible while faithfully conveying the original information, so as to improve the quality of the target text and assist medical development through translation.

The present paper describes a machine translation (MT) course taught to undergraduate students in the Department of English Language and Literature at Dhofar University in Oman. The course is one of the major requirements for BA in Translation. Fifteen EFL translation students who were in their third year of study were enrolled in the course. The author presents both the theoretical and practical parts of the course. In the theoretical part, the topics covered in the course are outlined. As for the practical part, it focuses on the translation students’ post-editing of online MT output. This is beneficial to the students as free online MT systems can potentially be used as a means for improving student translators’ training and EFL learning. This is achieved through subjecting MT output to analysis or post-editing by the students so that they can focus on the differences between the source and target languages. With this goal in mind, assignments were given to the students to post-edit the Arabic and English MT output of three free online MT systems (Systran, Babylon and Google Translate), discuss the linguistic problems that they spot for each system and choose the one that has the fewest number of errors. The results show that the students, with varying degrees of success, have managed to identify some linguistic errors with the MT output for each MT system and thus produced a better human translation. The paper concludes that there is a need to incorporate MT courses in translation departments in the Arab world, as integrating technology into translation curricula will have great effect on student translators’ training for their future career as professional translators.

The present study seeks to address the emotional lexical units in the context of Turkish synonyms that fall under the categories of terms for pain, joy, and excitement, in addition to examining potential English equivalents based on two machine translation tools: Google Translate and DeepL. Additionally, the researchers sought to identify whether two distinct machine translation systems presented near-synonyms in the same way, whether machine translation algorithms considered context when translating emotive lexical units, and how DeepL and Google Translate translated emotive lexical units differently. In this case, Google Translate and DeepL were used to assess sentences from the TDK online website that covered each category and had a total word count of 25. The findings have demonstrated that the machine translation systems DeepL and Google Translate typically produce translations that are comparable, that Google Translate platform considers context more when translating synonyms, and that Google Translate also considers words that are specific to Turkish or words borrowed from Arabic, Persian, and Ottoman Turkish, whereas DeepL occasionally failed in this area. Another finding is that machine translation tools have advanced to a great level, considering near-synonym translation. It is recommended that scholars benefit from the near-synonym corpora gathered in other subjects and their detections in other language pairs and that future research assess synonyms in other fields using machine translation or in various settings.

Technical and scientific texts as a form of written communication of scientific discourse have specific characteristic elements that are the subject of this analysis. Their peculiarity lies in their operation and use. The arsenal of these elements determines the choice of translation methods and techniques when processing and reproducing scientific and technical texts in the language of translation. It directly influences the choice of translation strategy and the drafting of the translation text, taking into account the differences between the two translation languages. The difference between the language of the French source text and the language of the translated text motivates the constant and growing need to promote research in the field of translation of technical and scientific texts into Ukrainian. The translation of technical and scientific texts is a complex creative process that aims to convey technical information from the original source into a language known to the user of the information. Therefore, the formation of skills of preliminary analysis of the source text of scientific discourse and its constituent elements contributes to the creation of adequate specialized texts written in Ukrainian. Based on our previous research on the classification system of basic translation transformations, we established a link between the characteristics of the French terminological system and the choice of Ukrainian translation techniques. Methods for translating technical and scientific texts are linked to the principle of functional equivalence, which in turn affects the quality of creation and editing of the translation text. The main indicator of the quality of scientific and technical translation is the adequacy of the original. An adequate translation is one that objectively, fully and accurately translates the meaning of the original text. The questions of definition and delimitation of the scientific and technical text, preliminary analysis of the source text and the system of its basic elements, as well as the question of analysis of the peculiarities of the use of translation transformations in translation Ukrainian language and general revision methods are the focus of our research. The analysis of these issues and the obtained results will be the subject of our further study and will help in the formation of the professional competence of the future translator for students of the first bachelor level of Ukrainian higher education.

This paper deals with machine translations of independent noun phrases in technical texts, which are not part of any sentence structure but function on their own, typically in tables and illustrations. Such nominal structures are common in technical texts because they allow technical writers to increase lexical density and precision in expression. On the other hand, these phrases pose a challenge for machine translation engines, as their meaning depends on the context. Independent noun phrases from a service manual, which were translated from English into Slovene by two different machine translators (DeepL and Google Translate), are considered in this paper. Their comparison with the original showed some limitations of machine translation engines in translating noun phrases, since approximately half of them showed a noticeable change in meaning.

Machine Translation (MT) has the potential to provide instant translation in times of crisis. MT provides real solutions that can remove borders between people and COVID-19 information. The widespread of MT system makes it worthy of scrutinizing the capacity of the most prominent MT system, Google Translate, to deal with COVID-19 texts into Arabic. The study adopted (Costa et al., 2015a) framework in analysing the output of Google Translate output service in terms of orography, grammar, lexis, and semantics. The study’s corpus was extracted from World Health Organization (WHO), United Nations Children’s Emergency Fund (UNICEF), U.S. Food and Drug Administration (FDA), the Foreign, Commonwealth & Development Office (FCDO), and European Centre for Disease Prevention and Control (ECDC). The paper reveals that Google Translate committed a set of errors: semantic, grammatical, lexical, and punctuation. Such errors inhibit the intelligibility of the translated texts. It also indicates that MT might work as an aid to translate general information about COVID-19, but it is still incapable of dealing with critical information about COVID-19. The paper concludes that MT can be an effective tool, but it can never replace human translators.

Machine translation is a machine that employs artificial intelligence (AI) to translate texts between languages without human intervention. Machine translation approaches translate text or speech from one language to another, including the contextual, idiomatic and pragmatic issues of both different languages. The present study aims to analyze the translation of literary texts selected from different novels, plays, and poems and clarify the method for translating them from English into Arabic. This study also aims to discover machine translation errors in rendering English literary texts and clarify the translator's role in transferring the rhetorical impact on the reader who reads the (TT). This study hypothesizes that translators(students) face difficulties regarding words and structures when translating literary texts from English into Arabic because they misunderstand rhetorical devices. So they tend to use machine translations that translate literally, such as (Google Translate, Reverso translation and Bing Microsoft translation). This study adopted two models: First, Newmark's translation model (1988b), which includes two basic types of translation: semantic and communicative. This model is used widely in the analysis of literary texts. Second, Harris (2018) linguistic model theory of rhetorical question and the general purpose of the rhetorical devices to analyze the data. Finally, the study ends with the conclusions that all machine translation programs (Google Translate (GT), Reverso Translation (Reverso. T), Bing Microsoft Translation (Bing. M.T) in rendering English literary texts from English into Arabic are unacceptable and have more problems because these programs are just machines and cannot think or feel as well as all these machines renderings are meaningless and ambiguous. So Human translation is better than Machine Translation because the first uses communicative translation while the other uses semantic translation.

This paper compared the syntactic complexity of translations from Arabic into English produced by Google Translate with human translation of the same texts using the web-based L2 Syntactic Complexity Analyzer (L2SCA). Syntactic complexity is an important aspect of writing quality and language proficiency in general. Understanding how machine translation tools deal with syntactic complexity when translating texts from Arabic into English will offer insights for their efficacy. The study analyzed the syntactic complexity of translations of five text samples collected from an Arabic book “Hiwar Ma’a Sadeeqi Almulhid” [Dialogue with An Atheist] by Mostafa Mahmoud. The output of Google Translate is compared with human translation for the same texts by Mohammed Yahia. The results showed that Google Translate output has lower syntactic complexity ratios for most of the 10 indices measured. The paper highlighted key findings that have implications for developing machine translation quality. This paper contributes to the ongoing development of machine translation.

With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy. However, since legal texts have features that pose major challenges to machine translation, the question arises as to what extent machine translation is now capable of translating legal texts or at least certain types of legal text into another legal language well enough so that the post-editing effort is limited, and, consequently, whether a targeted use in translation pedagogy can be considered. In order to answer this question, DeepL Translator, a machine translation system, and MateCat, a CAT system that integrates machine translation, were tested. The test, undertaken at different times and without specific translation memories, provided for the translation of several legal texts of different types utilising both systems, and was followed by systematisation of errors and evaluation of translation results. The evaluation was carried out according to the following criteria: 1) comprehensibility and meaningfulness of the target text; and 2) correspondence between source and target text in consideration of the specific translation situation. Overall, the results are considered insufficient to give post-editing of machine-translated legal texts a bigger place in translation pedagogy. As the evaluation of the correspondence between source and target text was fundamentally worse than with regard to the meaningfulness of the target text, translation pedagogy should respond by raising awareness about differences between machine translation output and human translation in this field, and by improving translation approach and strengthening legal expertise.

Recent availability of commercial online machine translation (MT) systems makes it possible for layman Web users to utilize the MT capability for cross-language information retrieval (CLIR). To study the effectiveness of using MT for query translation, we conducted a set of experiments using Google Translate, an online MT system provided by Google, for translating queries in CLIR. The experiments show that MT is an excellent tool for the query translation task, and with the help of relevance feedback, it can achieve significant improvement over the monolingual baseline. The MT based query translation not only works for long queries, but is also effective for the short Web queries.

Corpus-based machine translation (MT) has been the main approach to developing and implementing MT systems in both academia and the industry over the last three decades. In this field, the type and size of the corpus used for training MT engines have presented problems for both statistical MT (SMT) systems as well as neural MT (NMT) systems, being the two dominant corpusbased approaches. Moreover, language pairs such as Turkish-English have been understudied within this framework. This article aims to evaluate the translation quality in Turkish-to-English custom MT systems that have been trained on different corpus sizes and types. Two NMT engines and two SMT engines were trained on the KantanMT platform using two different training corpus types with either only domain-specific cardiology corpus or this corpus plus a mixed-domain corpus. The study conducted both automatic evaluations with metrics including BLEU, F-Measure and TER, as well as a comprehensive human evaluation with metrics including fluency, A/B test, and adequacy. Lastly, the study realized a separate, subjective terminology evaluation in order to investigate how differently MT systems handle terminology, as this is a crucial aspect for specific-domain text types such as cardiology. While the automatic evaluation results suggest the SMT engines to perform better than NMT engines, all human evaluators rated the mixed-domain NMT engine as the highest performing one. However, the terminology evaluation task demonstrated SMT to still be able to perform better and to commit less terminology errors, despite the industry and academia shifting toward NMT engines.

Translation as a form of language mediation is called upon to convert the source text written in one natural language into the target text in another, to assimilate or disseminate information across language barriers. Prior to the ubiquitous availability of the Internet, translators worked without the benefit of fast and affordable online access to up-to-date information in a wide variety of languages or to fellow translators in different geographical locations to share knowledge. Translation texts were largely in printed form and their circulation was dependent on physical delivery systems. In response to the widespread use of information and communication technology (ICT) leading to the advent of the Internet, the concept of teletranslation was first proposed (O’Hagan, 1996) to demarcate an emerging translation paradigm based on electronic networks of global communication rather than one where physical transportation was the main means for moving texts. It represented the new modus operandi where translators, which may include machine translation (MT), and customers are electronically linked with online access to translation tools and to other translators for knowledge sharing. It presupposes the translation text to be in electronic form and thus able to be seamlessly transmitted, stored, and processed by electronic means. Today, teletranslation has been embodied in Web-based platforms on the Internet, allowing the translator not only to receive and transmit translation text, but also to procure translation jobs, conduct research as well as collaborate in teams. Furthermore, an increasing array of computer applications has made computeraided translation (CAT) commonplace (Quah, 2006) where technology is indispensable for the production of translation. On the basis of the infrastructure supported by ICT networks as well as the prevailing use of CAT, teletranslation continues to develop as the predominant paradigm in the translation industry. The term translation embraces different modes of language mediation. According to the strict use of the terminology, however, “translating” refers to the act of mediating communication in written form, which typically takes place asynchronously whereas “interpreting” facilitates oral communication in synchronous mode as in conference interpreting. So far, the impact of ICT has been far more prominent in translation than in interpretation where text-processing tools play a limited role in oral communication and face-to-face mode prevails with little dependence on electronic means, with a few exceptions of remote interpreting modes. These language mediation modes serve to facilitate a given communicative situation where the sender and the receiver of the message do not share a common language, therefore requiring the source language to be converted into the target language in a required form. The language mediator plays the unique role of simultaneously being the receiver of the source text and the sender of the target text where translating or interpreting forms an act of communication embedded in another act of communication (Hatim & Mason, 1997, pp. 1–2). This fundamental role played by the language mediator remains the same in teletranslation. This article provides the background to the emergence of teletranslation, its current status, and future prospects with reference to relevant research and developments in related fields.

As artificial intelligence (AI) continues to redefine the boundaries of linguistic research, this study examines the extent to which machine translation (MT) and AI tools can go beyond literal meaning, push beyond surface-level syntax and semantics to process context-sensitive issues in literary translation. While traditional MT systems such as Google Translate and Microsoft Translator are optimized for direct source-to-target mapping, AI language models like ChatGPT represent a broader category of tools designed for general-purpose language generation, including but not limited to translation. Using a 14-line Arabic poem, translations were generated by three MT systems, one AI model (ChatGPT), and two certified human translators. These outputs were evaluated against ten linguistic and stylistic dimensions: punctuation, layout, rhyme, mood, theme, logico-semantics, transitivity, field, tenor, and mode. The six translation versions were compared using a framework grounded in systemic functional linguistics (SFL). The analysis also considers how humans process cognitive-linguistic features when rendering poetic language. Results indicate that ChatGPT outperformed both MT systems and human translators in structural and semantic coherence, as well as in preserving poetic features such as rhyme and mood. However, all automated systems struggled with context-rich dimensions like tenor and mode, underscoring the enduring value of human interpretive depth. The findings highlight the potential of AI language models to complement, rather than replace, human expertise in literary translation and advocate for hybrid approaches that integrate computational efficiency with poetic language and cultural sensitivity. Acknowledgements We would like to express our sincere appreciation to the human translators who contributed to this study by translating the selected poem from Arabic into English. We equally thank the two translation experts who contributed to the analysis of translations. Their careful attention to linguistic nuance and cultural context played a significant role in comparing the machine-generated and human-produced translations. The authors are thankful to the Deanship of Graduate Studies and Scientific Research at the University of Bisha for supporting this work through the Fast-Track Research Support Program. Disclosure Statement The authors reported no potential conflict of interests.

Purpose This study aimed to analyze the readability of Google Translate (GT) and Yandex Translate (YT) translation results on dialogue texts containing cultural terms from the book Antologi Cerita Anak Indonesia (ACAI). This study evaluated the effectiveness of the Neural Machine Translation (NMT) approach in GT and the Hybrid Machine Translation (HMT) approach in YT in conveying text meanings clearly and comprehensibly to readers.Method This research employed a cloze test involving 28 participants aged 18-24 years, along with a questionnaire to assess user preferences regarding GT and YT translation results. Text readability was analyzed using the Flesch-Kincaid Grade Level and Gunning Fog Index to measure the linguistic complexity of the translations.Results/Findings The study results show that GT's readability reaches 81.1%, while YT's readability is 74.5%, both categorized as the independent level according to Rankin & Culhane's (1969) criteria. Additionally, 80% of the 20 questionnaire respondents stated that GT's translations were clearer than those of YT. Analysis using the Flesch-Kincaid Grade Level and Gunning Fog Index shows that the readability level of GT and YT translations is classified as advanced suitable for readers with a minimum education level equivalent to a bachelor's degree.Conclusion This study showed that GT has a higher readability level than YT, which might be because of its use of NMT, producing more natural sentence structures. Meanwhile, YT, which also relied on SMT, translates based on statistical patterns, making its translations more rigid. Although both systems could produce comprehensible translations, they still struggled with accurately translating cultural terms without additional context. Therefore, human involvement remained essential to improving accuracy and contextual appropriateness in machine translation.