Machine Translation in Foreign Language Writing: Student Use to Guide Pedagogical Practice

This paper explores the experiences and perspectives of advanced language learners on the use of Machine Translation Tools (MTTs). The study focuses on how learners navigate their use of tools like Google Translate, DeepL, and Reverso, and how their usage is influenced by instructional recommendations. Nine learners, studying different languages at an advanced level, were interviewed in focus groups. Thematic analysis of the interview transcripts revealed key themes related to the use of Online Language Learning Tools (OLLTs) and MTTs, students' uncertainty about MTT functionalities, policies regarding MTT use, and suggestions for MTT training. The findings highlight the significant impact of initial encounters with MTTs on learners' long-term perceptions and use of these tools. Participants demonstrated strategic use of MTTs to support their reading, writing, listening, and speaking skills, while being aware of their limitations. Although learners found MTTs helpful, they also emphasised the importance of authentic communication in language learning. The study concludes with recommendation for a collaborative approach between teachers and learners to effectively integrate MTTs into the language learning curriculum.

This study investigated the impact of an intervention utilizing ChatGPT in literary translation on EFL student translators’ comfort level, proficiency in machine translation (MT) tools, openness to embrace AI technology, and self-efficacy in literary translation. Additionally, the study aimed to identify the benefits and limitations reported by the participants in using ChatGPT for literary translation. Using an experimental pre-post design and purposive non-probability sampling, 50 student translators participated in the study. Data collection instruments included a pre-post perceived self-efficacy questionnaire and follow-up semi-structured interviews. Quantitative data analysis indicated the effectiveness of the study intervention in enhancing student translators’ proficiency in using MT tools and their comfort level with them. Significant differences were observed in student translators’ self-efficacy beliefs before and after the intervention. Additional analysis demonstrated a significant difference in student translators’ confidence before and after the intervention, accompanied by a medium effect size. The analysis of participants’ responses to the questionnaire open questions revealed notable benefits and limitations associated with using ChatGPT in literary translation. Furthermore, the qualitative analysis of the interview data yielded valuable insights into the transformative effect of the ChatGPT intervention on students’ openness, comfort and confidence in using AI tools in literary translation.

Focusing on the topic of applying machine translation (MT) to Chinese–English subtitling, the subject of the present study, this article first analyses the current literature and introduces the general constraints in Chinese–English subtitling from three perspectives: technical, cultural, and textual. Technical constraints are imposed by the limited time and space available for each subtitle; cultural constraints relate to the disparities in the beliefs, values, customs, behaviours, and artefacts of different cultural groups; and textual constraints predominantly manifest in the differences between the source and target languages and the segmented nature of subtitles. In order to respond to these constraints and to achieve conciseness, comprehensibility, and coherence in the translated subtitles, this study highlighted condensation, context, and coordination as the key strategies to adopt. However, these strategies pose considerable challenges for MT in Chinese–English subtitling. First, the common practice of full translation by most MT tools tends to work against making subtitles concise. Second, the lack of relevant contextual knowledge limits the ability of MT to generate appropriate translations. Third, the segmented display of subtitles makes it challenging for MT to capture and reflect the logic in the source text, on the one hand, and to produce coherent output in self-contained segments, on the other. To illustrate these constraints and challenges, this article provides examples of bilingual subtitles from American Factory (Bognar & Reichert, 2019), an Oscar Winner for Best Documentary Feature, and compares the official subtitles with those generated by three popular MT tools in Chinese–English translation. The study investigated the efficacy of MT subtitling and its potential to produce quality subtitles; and this article proposes possible solutions and suggestions for improving the quality of MT in subtitling.

This article discusses the current requirement for scientific research to be published in the English language and the problems arising from this requirement not only for authors whose first language is not English but also for society and for the world's scientific community. The assistance which is and should be available to authors is considered as well as a future looking towards multilingual publication.

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.

With the rapid development and popularisation of AI technology, AI tools are gradually and frequently used in students' learning processes. In the field of college students' foreign language learning, especially in the field of writing, the assistance that AI can give is particularly significant. Foreign language writing as language output and language learners comprehensive knowledge literacy, social interaction, psychological identity, cultural understanding and other aspects of the complexity of the integrated embodiment. Therefore, the use of artificial intelligence to assist college students in writing foreign languages has aroused discussion and attention. Focusing on the field of college students' foreign language writing ability and teaching, the purpose of this paper is to review the research on Artificial Intelligence (Chat GPT) assisted foreign language writing ability of college students and explore the opportunities and challenges of this technology. Writing is an indispensable ability for foreign language learners, and this study is of great significance for college students as a key group of people who drive the development of society. This paper will use literature analysis to understand the application of artificial intelligence in college students' foreign language writing, focusing on the advantages and disadvantages of the use of this technology. Finally, corresponding conclusions will be drawn based on the results of the study.

Modeling workflow to design machine translation applications for public health practice

The objective of the research is to review the ability of online machine translator tools includes Google Translate (GT), Collin Translator (CT), Bing Translator (BT), Yandex Translator (YT), Systran Translate (ST), and IBM Translator (IT). This research applies descriptive qualitative. The documentation was used in this study. The result of the analysis shows that the translation results are different, both from the style of language and the choice of words used by each machine translation tool. Thus, directly or indirectly, whether consciously or not, each translation machine carries its characteristics. Machine translation technology cannot be separated from the active role of humans. In other words, it will always be the best choice for users to rely on expert translation rather than machine translation. But no machine translator can be as accurate as human skills in producing translation products. In particular, the field of translation is also concerned with machine translation to support the performance of translators in analyzing the diction used as an element of language. In this regard, it needs to be underlined that the existence of machine translation is an additional facility in the world of translation, not as the main means of translation because the sophistication of the machine will not be able to match the flexibility of the human brain's cognitive abilities in adjusting the translation results according to the existing context. Accurate translation is sometimes subjective, relatively often temporal. Therefore, it is permissible for translating by more than one machine translator

Machine Translation (MT) should be integrated into Master of Translation and Interpreting (MTI) translation teaching more intelligently. This study focuses on the practical application of MT in MTI translation teaching, aiming to overcome challenges such as the rigidity in traditional teaching models, the monolithic methods for translation quality assessment, and the shortage of qualified teachers. Through an analysis of the current status of MTI translation teaching, this paper proposes strategies like utilizing MT for multi-modal teaching, conducting post-translation quality assessment training with MT, and implementing teacher training with MT tools. The results indicate that these strategies not only significantly improve teaching quality and efficiency but also promote innovation in teaching models. This paper offers novel insights into the MTI translation teaching method and provides solid support for the deep integration of MT with educational practice, thereby ushering in an important pathway for cultivating outstanding translation talents in the new era.

The web as corpus is considered helpful in translation training, although the literature argues that Internet data might be too chaotic and badly organised.For this reason, web concordancers are often resorted to in order to carry out web searches as they are more linguistically oriented and user-friendly.At the same time, machine translation (MT) has made huge strides forward in the last decades, and scholars have increasingly focused on MT in technical translation training.However, so far little has been investigated on the quality of MT vis--vis translations via web concordancers.This paper wishes to bridge this gap and aims at exploring whether and to what extent the web-based translation of a medical article carried out by consulting the Leeds web-concordancer is qualitatively more satisfactory than a translation using MT, or vice-versa.To this aim, a classroom observation study is undertaken with 9 Italian final-year bachelor's students.The students' Leeds-driven translations are compared with the automatic translations performed by two MT tools (DeepL and MateCaT).The paper findings highlight the overall correctness of the machine-translated sentence structures as well as of the related grammar.However, MT presents lexical and collocational issues mostly due to infrequent word usages.By contrast, the web-driven students' translations present a wider variety of lexical and collocational choices, despite some grammatical shortcomings.The paper suggests further research to investigate whether more training in translations based on web concordancers can help students feel at ease with online searches and reduce inaccuracies.

This book explains the concept, framework, implementation, and evaluation of controlled document authoring in this age of translation technologies. Machine translation (MT) is routinely used in many situations, by companies, governments, and individuals. Despite recent advances, MT tools are still known to be imperfect, sometimes producing critical errors. To enhance the performance of MT, researchers and language practitioners have developed controlled languages that impose restrictions on the form or length of the source-language text. However, a fundamental, persisting problem is that both current MT systems and controlled languages deal only with the sentence as the unit of processing. To be effective, controlled languages must be contextualised at the document level, consequently enabling MT to generate outputs appropriate for their functional context within the target document. With a specific focus on Japanese municipal documents, this book establishes a framework for controlled document authoring by integrating various research strands including document formalisation, controlled language, and terminology management. It then presents the development and evaluation of an authoring support system, MuTUAL, that is designed to help non-professional writers create well-organised documents that are both readable and translatable. The book provides useful insights for researchers and practitioners interested in translation technology, technical writing, and natural language processing applications.

Artificial Intelligence or AI as it is popularly known and its corollary, Machine Translation (MT) have long engaged scientists, thinkers and linguists alike in the twenty first century. However, the wider question that lies in the relation between technology and translation is, What does technology do to language? This is an important question in the current paradigm because new translation technologies, such as, translation memories, data-based machine translation, and collaborative translation, far from being just additional tools, are changing the very nature of the translators’ cognitive activity, social relations, and professional standing. In fact, in some translation situations such as when translating technical materials or subject matter that are not a specialization with human translators, one potentially needs technology. The purview of this paper, however, is limited to the role of MT in day to day situations where the generic MT tools like Google Translate or Bing Translator are encouraged. Further, it endeavours to weigh and empirically demonstrate the pros and cons of MT with a view to recommending measures for better communication training in the EFL set up of Saudi Arabia. Keywords: AI, MT, translation, technology, necessity, communication

This paper presents a study of the negative effect of Machine Translation (MT) on the precision of Cross---Lingual Question Answering (CL---QA). For this research, a English---Spanish Question Answering (QA) system is used. Also, the sets of 200 official questions from CLEF 2004 and 2006 are used. The CL experimental evaluation using MT reveals that the precision of the system drops around 30% with regard to the monolingual Spanish task. Our main contribution consists on a taxonomy of the identified errors caused by using MT and how the errors can be overcome by using our proposals. An experimental evaluation proves that our approach performs better than MT tools, at the same time contributing to this CL---QA system being ranked first at English---Spanish QA CLEF 2006.

The use of the first language (L1) in the English as a foreign language (EFL) classroom is becoming an on-going debate. A newly growing view to the use of L1 in the foreign language (FL) classroom called translanguaging views that by utilizing the L1 along with the TL in FL classroom is considered as functional practice of languages rather than an impediment in FL learning. Accordingly it is necessary to find out the functions of teachers’ practice of translanguaging in the EFL classroom since this view has not been much researched yet. Related to this, the current study was intended to find out the functions of the EFL teachers’ translanguaging and to find out the teachers’ reasons for the use of translanguaging. This study followed qualitative descriptive interactive research design, and the subjects consisted of three English teachers at the 7th grade classes at two junior high schools in Singaraja. The data were collected through observations and interviews and were analyzed descriptive qualitatively. The results of this study showed that there were 3 functions of translanguaging. The most frequent function was related to knowledge construction, followed by classroom management, and interpersonal relations. There were 9 reasons for the teachers’ use of translanguaging, namely: to facilitate students’ understanding, to provide L1 and TL comparison, to elicit students’ responses, to attract students’ attention, tomanage students, to promote discipline, to develop deeper personal relationship, to create secure classroom atmosphere, and to make the class more interesting. In sum, the use of translanguaging plays a number of functions in the EFL classroom but it should be considered also the ‘optimal’ use of it in EFL learning process because the overuse of L1 might bring negative impact.

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.