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How closely does the translation match the meaning of the reference has always been a key aspect of any machine translation (MT) service. Therefore, the primary goal of this research is to assess and compare translation adequacy in machine vs human translation (HT) from Arabic to English. The study looks into whether the MT product is adequate and more reliable than the HT. It also seeks to determine whether MT poses a real threat to professional Arabic–English translators.

Six different texts were chosen and translated from Arabic to English by two nonexpert undergraduate translation students as well as MT services, including Google Translate and Babylon Translation. The first system is free, whereas the second system is a fee-based service. Additionally, two expert translators developed a reference translation (RT) against which human and machine translations were compared and analyzed. Furthermore, the Sketch Engine software was utilized to examine the translations to determine if there is a significant difference between human and machine translations against the RT.

The findings indicated that when compared to the RT, there was no statistically significant difference between human and machine translations and that MTs were adequate translations. The human–machine relationship is mutually beneficial. However, MT will never be able to completely automated; rather, it will benefit rather than endanger humans. A translator who knows how to use MT will have an opportunity over those who are unfamiliar with the most up-to-date translation technology. As MTs improve, human translators may no longer be accurate translators, but rather editors and editing materials previously translated by machines.

The findings of this study provide valuable and practical implications for research in the field of MTs and for anyone interested in conducting MT research.

In general, this study is significant as it is a serious attempt at getting a better understanding of the efficiency of MT vs HT in translating the Arabic–English texts, and it will be beneficial for translators, students, educators as well as scholars in the field of translation.

This study aims to examine the effects of using mobile translation applications for translating collocations.

The study followed an experimental design where 47 students of English as foreign language in a Saudi university were randomly categorized into two groups. Both the groups were given a translation task consisting of 30 sentences with fixed, medium-strength and weak collocations. The participants in the experimental group (n 23) were asked to use a mobile App (Reverso) to translate the sentences, while the control group (n 24) was allowed to use only paper-based dictionaries. The translations were scored and analyzed to measure if there was any significant difference between the two groups.

The results indicated that the mobile translation application was more effective in translating fixed and medium-strength collocations than weak collocations, and in translating collocations in both translation directions (i.e. from Arabic into English or vice-versa).

The findings suggest that integrating translation technologies in general and mobile translation applications in particular in translation can enhance the translation process. Students can utilize mobile translation applications to enhance their translation skills, especially for translating collocations.

The integration of the Internet in translation creates several opportunities for translators. This study aims at examining the impact of using web-based translation (WBT) on translating religious texts.

The study followed a quasi-experimental study design. Sixty students enrolled in English Department, University of Bisha, participated in this study. The participants were divided randomly into three groups (i.e. words group, sentences group and passages group). The data was collected through a translation test and a questionnaire.

The results indicated that WBT is more beneficial in translating words than translating sentences or passages. In addition, WBT is more beneficial when words are translated from English into Arabic as well as from Arabic into English. The results from the questionnaire revealed positive attitudes toward using WBT in the process of translation.

This use of technology in translation has been examined in many studies (e.g. Bundgaard et al., 2016). WBT can be used to translate any field of knowledge. One of these fields is religious translation. According to O'Connor (2021), the study of religious translation has expanded greatly in recent years from its strong textual tradition and a constant focus on equivalence and translatability. However, very little has been done to examine the impact of WBT on translating religious texts. Therefore, this study aims at exploring the impact of WBT on translating religious texts with special reference to Islamic texts.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

The spread of Covid-19 has led to the closure of educational institutions worldwide, forcing academic institutions to find online platforms. The purpose of this paper is to accelerate the development of the online learning (OL) environments within those institutions. The Covid-19 pandemic has unfolded the extent of the academic institutions' readiness to deal with such a crisis.

In this vein, the study aimed to identify the perception of translation instructors in teaching translation courses online during Covid-19, using a questionnaire to explore the strategies and challenges of teaching and assessing students' performance. The analysis revealed instructors' reliance on Zoom and Microsoft Teams in offering virtual classes and WhatsApp in communication with students outside the class.

The findings revealed the relative effectiveness of online education, but its efficacy is less than face-to-face learning according to the respondents' views. It was also found that students faced difficulties in OL, which lie in adapting to the online environment, lack of interaction and motivation and the deficiency of data connections. Even though online education could work as an aid during Covid-19, but it could not replace face-to-face instruction. Based on the findings, the study recommended blended learning. Combining online education with face-to-face instruction, i.e. face-to-face plus synchronous and asynchronous, would result in a rigorous OL environment.

The research is genuine and there is no conflict of interest.

Effective translation has become essential for seamless cross-cultural communication in an era of global interconnectedness. Translation management systems (TMS) have redefined the translation landscape, revolutionizing project management and execution. This study examines the attitudes of translation agencies and professional translators towards integrating and utilizing TMS, with a specific focus on Saudi Arabia.

The study's design was based on a thorough mixed-methods strategy that purposefully combined quantitative and qualitative procedures to create an array of findings. Through a survey involving 35 participants (both project managers and professional translators) and a series of interviews, this research explores the adoption of TMS, perceived benefits, influencing factors and future considerations. This integrated approach sought to investigate the nuanced perceptions of Saudi translation companies and expert translators about TMS. By combining the strengths of quantitative data's broad scopes and qualitative insights' depth, this mixed-methods approach sought to overcome the limitations of each method, ultimately resulting in a holistic understanding of the multifaceted factors shaping attitudes within Saudi Arabia's unique translation landscape.

Based on questionnaires and interviews, the study shows that 80% of participants were familiar with TMS, and 57% had adopted it in their work. Benefits included enhanced project efficiency, collaboration and quality assurance. Factors influencing adoption encompassed cost, compatibility and resistance to change. The study further delved into participants' demographic profiles and years of experience, with a notable concentration in the 6–10 years range. TMS adoption was linked to improved translation processes, and participants expressed interest in AI integration and mobile compatibility. Deployment models favored cloud-based solutions, and compliance with industry standards was deemed vital. The findings underscore the evolving nature of TMS adoption in Saudi Arabia, with diverse attitudes shaped by cultural influences, technological compatibility and awareness.

This research provides a holistic and profound perspective on the integration of TMS, fostering a more comprehensive understanding of the opportunities, obstacles and potential pathways to success. As the translation landscape continues to evolve, the findings from this study will serve as a valuable compass guiding practitioners and researchers towards effectively harnessing the power of technology for enhanced translation outcomes.

Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the cross-language studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentence-alignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages.

Clinical notes typically contain medical jargons and specialized words and phrases that are complicated and technical to most people, which is one of the most challenging obstacles in health information dissemination to consumers by healthcare providers. The authors aim to investigate how to leverage machine learning techniques to transform clinical notes of interest into understandable expressions.

The authors propose a natural language processing pipeline that is capable of extracting relevant information from long unstructured clinical notes and simplifying lexicons by replacing medical jargons and technical terms. Particularly, the authors develop an unsupervised keywords matching method to extract relevant information from clinical notes. To automatically evaluate completeness of the extracted information, the authors perform a multi-label classification task on the relevant texts. To simplify lexicons in the relevant text, the authors identify complex words using a sequence labeler and leverage transformer models to generate candidate words for substitution. The authors validate the proposed pipeline using 58,167 discharge summaries from critical care services.

The results show that the proposed pipeline can identify relevant information with high completeness and simplify complex expressions in clinical notes so that the converted notes have a high level of readability but a low degree of meaning change.

The proposed pipeline can help healthcare consumers well understand their medical information and therefore strengthen communications between healthcare providers and consumers for better care.

An innovative pipeline approach is developed to address the health literacy problem confronted by healthcare providers and consumers in the ongoing digital transformation process in the healthcare industry.

The current study set out to examine the recruitment practices and job-market readiness of translators in Saudi Arabia in light of the Process in the Acquisition of Translation Competence and Evaluation (PACTE) model of translation competence (TC). The main purpose of the study was to determine the extent to which the outcomes of translator training programs are aligned with job-market requirements.

A case study was conducted adopting a mixed-methods research design to collect qualitative and quantitative data using interviews and a questionnaire. Data were also collected from the analysis of 28 translation job advertisements. The questionnaire targeted employers, while the interviews were conducted with employers and professional translators.

The findings indicate that there is a gap between the outcomes of translator training programs and the needs of the job market with a particular emphasis on the importance of developing trainee translators' job-market skills as well as their awareness of the professional practice of translation.

The findings of the current study are limited to the sample from which data were collected.

The study has significant implications for translators and translator training in Saudi Arabia. Although the Literature, Publishing and Translation Commission and the Saudi Translators' Association were established in 2020, additional measures are needed to support the translation profession in Saudi Arabia. Such support may take the form of specialised accreditation for translator training programs as well as licensing requirements for practitioners. Implementing these measures will play a significant role in establishing benchmarks for translator training programs and promoting the integration of job-market requirements into translator training.

Although TC has been examined in the Saudi context before, examining it in light of the PACTE framework sheds new light on the job-market readiness of translation program graduates and enriches the literature on the training of translators in Saudi Arabia.

The purpose of the current study was to examine the role of distance learning in enhancing introverted students’ lack of communication and social interaction to improve their performance in translation class. Cain (2013) and Kuzeljevich (2017) agreed that identifying “introverted” and “extroverted” students is important for meeting their learning needs. While extroverted students have strong social skills that allow them to interact comfortably in different learning environments, introverted students tend to be more shy, quiet, and silent, thus, requiring more careful planning in classroom settings. Therefore, educators need to support introverted students in reaching their full academic and social potential.

The present case study adopted a qualitative research method to explore the role of online/distance learning during the COVID-19 pandemic in enhancing introverted students’ performance and communication abilities in translation classes. The researcher of the current study spent a considerable time observing and set herself as part of the group (i.e. translation students of level 6 class) to understand the phenomenon, events and the new situation of having translation students interact in online settings. Data collection was based on this observation, interviews with the participants and archival documents. To enhance the validity and credibility of this research, the researcher employed the method of triangulation.

The results (see Appendixes A, B and C) revealed the level of students interactions in translation classes and their attitudes toward online learning. Based on the observations made by the instructor, the researcher found that the involvement of the introverted students during online translation learning was remarkable, as they provided their translation outputs in the chat window of Microsoft Teams with no hesitation. Consequently, 65% of the students were providing their translation output through the chat window, which indicates that they are more introverted and preferred not to speak. Comparing this result to face-to-face translation class, the researcher found that 25% of the students provided their translation outputs through oral participation.

This study contributes to the field of translation and education. Previous studies have not sufficiently examined the role of distance learning in enhancing the performance and communication of introverted students in translation classes. The current study is also expected to provide insight into the field of technical translation in remote teaching and learning settings.