Translation Workflow Research Articles

Toolkit for Translating Health Information: A Resource for Crisis Health Communication and beyond

Abstract Issue/Problem Translating health information accurately and effectively presented a critical challenge in crisis communication during the pandemic. In linguistically diverse contexts, ensuring access to vital health information is essential for promoting public health outcomes. Addressing this issue, we summarized our experience in a comprehensive toolkit for translating health information, aiming to enhance accessibility, reliability, and relevance across various settings and countries. Description of the Problem Our initiative responds to the need for standardized translation practices in the dissemination of health information in Norway. Translation practices in the health sector often lack standardization, leading to inconsistencies, inaccuracies, and risk of less comprehension. Questions guiding our project included: How can lessons learned from the extensive translation work done during the pandemic improve the accuracy and consistency of health information translation in general? Results We have developed a practical toolkit tailored to diverse linguistic and cultural contexts. Our toolkit summarizes lessons learned from extensive translation work during the pandemic, supported by validation from experts in the field of translation. The toolkit has been welcomed by other organizations, which underscores the potential of standardized translation tools to mitigate barriers to health communication in multilingual settings. Lessons Lessons gleaned from translating information during the pandemic highlight the importance of evidence-based translation practices, knowledge of the target demographic and the integration of cultural considerations into translation workflows. Furthermore, our experience underscores the value of inclusive approaches in development, promotion and distribution of the translated materials. Key messages • Standardized translation tools can increase access to vital health information, fostering equitable health outcomes. • Collaboration and inclusive approaches are key drivers in advancing standardized translation practices for effective health communication.

Translating Four-Character Structures in Chinese Literary Works on Traditional Architecture—A Case Analysis of Canal Towns South of the Yangtze and Folk Houses South of the Yangtze

Traditional Chinese architecture boasts a unique style and profound cultural connotations. Accordingly, the related literary works attain value for international dissemination. The four-character structures in these works serve as concise expressions for describing architectural details and design concepts. However, due to their linguistic implicitness, grammatical structure diversity, and rich cultural images, their translation into English requires multidimensional shift, redundancy elimination, and the avoidance of cultural defaults. This research has developed an effective translation workflow that leverages the synergistic strengths of Large Language Models (LLMs) and the human translator. By strategically employing tailored methods and techniques, it addresses the challenges in the translation process. Analysis suggests: It is recommended that the technique of shift be employed to address the implicit content and complex grammatical structures in translating four-character structures in Chinese literary works on traditional architecture into English. Besides, the selective application of combination and omission can help deal with information redundancy. Furthermore, it is advisable to apply foreignization and a combined method of literal translation and paraphrasing in conveying cultural images. These approaches will, to a certain extent, convert the untranslatability of four-character structures into translatability, enhancing the dissemination of the essence of traditional Chinese architecture.

A Study on How Globalization Influences Translation Practices and the Demand for Translators in Various Industries

This study investigates the impact of globalization on translation practices and the demand for translators across various industries. Utilizing a mixed-methods research design, the study combines quantitative surveys and qualitative interviews with professional translators, translation agency managers, and industry experts. The findings reveal that technological advancements, particularly machine translation (MT) and computer-assisted translation (CAT) tools have significantly altered translation workflows, increasing efficiency while necessitating careful quality control. The demand for localization services has surged, especially in the tech industry, reflecting a broader trend towards culturally adapted content in global markets. Additionally, the study highlights a notable shift towards freelancing and remote work among translators, driven by the desire for flexibility but accompanied by challenges such as job stability and securing consistent work. Emerging challenges identified include maintaining translation quality amidst high workloads, adapting to rapidly evolving technologies, and navigating cultural differences to ensure culturally appropriate translations. Economic pressures, such as low rates and payment issues, further complicate the landscape for professional translators. These findings align with existing literature, emphasizing the need for continuous adaptation, professional development, and strategic approaches to address the dynamic and evolving nature of the translation industry in a globalized context. The study provides valuable insights into the current state of the translation industry, highlighting the importance of cultural competence, technological proficiency, and economic resilience for translators operating in a globalized world. Future research should explore the long-term impacts of these trends and the evolving role of translators in a rapidly changing global market.

Indirect (pivot) audiovisual translation: a conversation with and among B. Agulló Garcia, D. Orrego-Carmona and G. Spiteri Miggiani

ABSTRACT Through a series of questions and responses, in this dialogue-based article we aim to stimulate reflection on indirect translation within the audiovisual translation community, offering perspectives on this practice. We start by delving into the historical roots of indirect translation in the industry, questioning how far back such practices extend. Then we shift the focus to the challenges translators face in indirect translation workflows, to the potential contributions of research to the ongoing debates surrounding indirect translation, as well as to how indirect translation and machine translation intersect. Finally, looking to the future, we examine potential developments in indirect translation workflows and consider how educational programs can adapt to meet the changing demands of the industry.

Does Translation Technology Affect Translators' Performance? A Meta-Analysis

Translation technologies, including computer-assisted translation (CAT) tools, translation memory (TM) systems, and machine translation (MT), are increasingly utilized in professional translation workflows and training. However, the effects of these technologies on translators' performance remain inconclusive. This meta-analysis examines the overall impact of translation technologies on translator performance by synthesizing data from 12 experimental studies published between 2000 and 2023. The study investigates the effectiveness of translation technologies compared to traditional translation methods. The findings reveal a significant positive effect size, indicating that translation technologies have the potential to improve translators’ performance relative to purely human translation. The integration of advanced interactive CAT systems and post-editing MT demonstrates larger advancements compared to basic TM match retrieval. Moreover, experienced professional translators derive greater benefits from incorporating technologies than student translators, highlighting the importance of leveraging automation capabilities alongside human expertise. However, the study identifies significant heterogeneity among the studies, influenced by factors such as translation direction. Translators translating into their native language exhibit greater advancements, emphasizing the advantages of technologies that strengthen fluency in the target language.

Innovative methods for integrating translation memory and CAT tools: Enhancing intelligent support in human translation processes

This paper explores the innovative integration of Translation Memory (TM) and Computer-Assisted Translation (CAT) tools to enhance translation efficiency, consistency, and quality in multinational organizations. By adopting a user-centric interface design, modular system architecture, and cloud-based deployment, the integrated system addresses diverse translation needs while ensuring data security and privacy. Key features include real-time translation suggestions powered by machine learning algorithms, seamless access to TM databases, and real-time collaboration tools. The paper discusses implementation strategies, challenges, and solutions, highlighting the importance of user training and continuous improvement. A case study demonstrates significant improvements in translation speed, accuracy, and user satisfaction, underscoring the potential of advanced translation technologies to transform translation workflows. The findings provide valuable insights into best practices for successful implementation and optimization of TM and CAT tools in complex, large-scale environments.

Cultural ergonomics in localization

Abstract Cultural ergonomics aims to integrate the dimension of culture into systems and products to make them safer, more useful, and more accessible to a wider range of multicultural users. This makes it an effective conceptual tool to create a localized product that is more inclusive. By using the example of security questions in drop-down menus replicated across North American English websites, this paper argues that overlooking ‘culture’ as a site of diverse experiences leads to a harder-to-navigate product for the multicultural user. By juxtaposing the reality of a multicultural context with the monoethnic, middle-class, and heteronormative cultural dimensions of security questions, the cloning of questions across websites can be seen as satisfying the needs of the dominant power rather than the subordinated peoples – both immigrant and non-heteronormative. Operationalizing cultural ergonomics in the translation workflow provides a means to recognize and address this power imbalance.

Research on the Acceptance of Crowdsourced Translation Platform Technologies by Translators

In the current era marked by rapid digitalization and globalization, crowdsourced translation platforms have emerged as key players in the global translation market, prized for their flexibility and efficiency. This new model of translation services not only provides a means to address large-scale translation demands but also introduces challenges and shifts in conventional translation workflows. This study investigates translators’ acceptance of paid crowdsourced translation platform technologies, with the aim of identifying the primary factors that influence technology acceptance and understanding how these factors impact translators’ work attitudes and behaviors. Through a mixed-methods approach that integrates surveys and in-depth interviews, the study gathers data from 300 translators with varied experiences and backgrounds. Utilizing the Technology Acceptance Model (TAM), the research reveals the significant effects of perceived usefulness and perceived ease of use on translators’ acceptance of technology. It is particularly noteworthy that translators show a high sensitivity to their perceptions of the platforms’ efficiency and potential changes in their income. Furthermore, job satisfaction emerges as a key factor in determining translators’ willingness to continue using crowdsourced platforms. This research not only provides insights into how to enhance the relationship between crowdsourced translation platforms and translators but also offers valuable recommendations for the advancement of translation education and the professional development of translators.

Machine translation literacy in the legal translation context: a SWOT analysis perspective

ABSTRACT In the past couple of decades, machine translation (MT), whether phrase-based statistical or neural MT, has made considerable progress and has become increasingly common in specialised translation workflows. More studies on MT in legal contexts have been conducted in the past several years, and MT has been and continues to be used in international and supranational institutions that rely on legal translation and whose voluminous collections of translated text have been used to train contemporary systems. The time is ripe for a synthesis of developments in the area that may reinforce our understanding of what we might expect from MT from the perspective of legal translators. This systematic review of legal translation and MT studies teases out potential strengths/weaknesses of MT and opportunities/threats of using it from a legal translator perspective, focusing on lexical and morphosyntactic challenges that may set legal translation apart from other specialised translation areas. A SWOT analysis from this perspective may offer value as a pedagogical framework for fostering domain-specific MT literacy in the legal translation classroom. Students will develop an understanding of what might reasonably be expected from MT in legal translation contexts, while reinforcing their understanding of legal translation challenges.

LEARNING ACTIVITIES FOR TEACHING PROFESSION SPECIFIC TRANSLATION TO STUDENT TEACHERS OF ENGLISH ON THE TOPIC OF TEACHERS AND LEARNERS: NEW ROLES AND COMPETENCES.

LEARNING ACTIVITIES FOR TEACHING PROFESSION SPECIFIC TRANSLATION TO STUDENT TEACHERS OF ENGLISH ON THE TOPIC OF TEACHERS AND LEARNERS: NEW ROLES AND COMPETENCES.

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

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

Enhancing CAT education: the significance of teaching the right skills in the right time

This article explores the dynamic landscape of Computer-Assisted Translation (CAT) tools, aiming to identify the key skills employed by language professionals and the essential competencies students should acquire. Employing a survey methodology, responses from a diverse pool of translators shed light on CAT tool preferences, termbase utilization, and automation practices. RWS Trados and memoQ emerge as frontrunners, underlining the versatility required in contemporary translation workflows. While over 80% adopt quality assurance features and ensure precise tag transfers, challenges persist in reusing termbases across projects.ile conversion skills, particularly.tmx conversions, prove significant for translation memory management. Automation practices such as term extraction and pretranslation reveal varied adoption, signaling opportunities for tailored education. Despite limited current usage, machine translation plug-ins and AI present strategic advantages for translators in training, with the emergence of technologies like ChatGPT anticipated to drive a surge in usage Encouragingly, the article concludes that the emergence of AI does not diminish CAT tool usage, positioning them as integral to the evolving language services landscape.

Impact of Fine-Tuned Machine Translation on Medical Documentation Translation and Client Satisfaction

Aim: The aim of this study was to assess the impact of Fine-Tuned Machine Translation (FTMT) models, followed by Machine Translation Post-Editing (MTPE), on the translation quality and client satisfaction in medical documentation. Materials and Methods: The research analyzed 733,632 words across 317 projects completed in 2023 by Lingrowth, a medical and life science translation service provider. These projects involved translations from 16 source languages to 34 target languages. Document types included Instructions for Use (IFU), Investigator Brochures (IB), Summary of Product Characteristics (SmPC), Clinical Study Reports (CSR), and Informed Consent Forms (ICF). The projects were categorized into two groups for comparison: translations performed by human translators using Translation Memory (TM) and those processed by FTMT followed by MTPE. The primary metrics for evaluation were the number of post-project finalization requests and instances of severe negative feedback from clients. Results: It was observed that human-translated projects had a higher frequency of post-project finalization requests, whereas MTPE projects after FTMT exhibited a marginally higher rate of severe negative feedback. However, statistical analysis indicated that these differences were not significant, suggesting that the introduction of FTMT models with subsequent MTPE does not adversely affect the overall quality of medical document translations. Conclusion: The study concludes that FTMT models, when supplemented by MTPE, are com-parable in effectiveness to traditional human translations in the context of medical documentation. This highlights the potential of integrating FTMT in translation workflows without compromising translation quality or client satisfaction.

Technology-Based Platforms in the Translation Workflow: An Investigation of the Use of CAT Tools among Saudi Professional Translators

Technology-Based Platforms in the Translation Workflow: An Investigation of the Use of CAT Tools among Saudi Professional Translators

Integration and Innovation of Artificial Intelligence and Traditional English Translation Methods

Abstract In this study, we employ autoregressive and neural machine translation models to process natural language. Our exploration of AI translation workflows encompasses three key aspects: the encoder-decoder framework, text feature representation, and translation derivation methodologies. Subsequently, we identify the core evaluation indices essential for assessing intelligent translation systems. An innovative translation model is constructed by amalgamating AI-driven and traditional English translation techniques. This model is scrutinized for its effectiveness through various lenses, including the quality of English-to-Chinese poetry translations and manual evaluations. Comparative analysis of the proposed translation method with other extant methods across diverse datasets reveals superior performance metrics: the BLEU score consistently exceeds 4.5 across all three test sets, and the METEOR score ranges between 4.3 and 4.6, surpassing competing methods. Additionally, translation accuracy for sentences of varying lengths in the source language is maintained between 85% and 96%. The objective of this research is to conduct a comprehensive examination of the integration of AI English translation with conventional methodologies, aiming to foster innovative theoretical contributions and practical advancements in the field.

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.

Is individual competition in translator training compatible with collaborative learning? The case of the MTIE Translation Award

Abstract In past years, translation education has shifted from a “transmissionist approach” to the unchallenged use of collaborative learning, with extensive recourse to teamwork and Project-based Learning. Students are encouraged to develop their translation and interpersonal skills in collaborative environments, focusing on translation as a process. Throughout this process, mistakes are valued as learning opportunities, and no translated text is seen as an authoritative version to which all others are unfavourably compared. This approach is somewhat at odds with an increasingly competitive job market, in which translators must be capable of autonomous and individual work, and translation is mainly viewed and evaluated as a final product. Although translation workflows involve a growing amount of group effort, translators still need to work alone and take responsibility for their own versions and translation choices. In order to prepare students to become professional translators, the annual Technical and Scientific Translation Award aims not only to give winners visibility before potential employers but also to work as an opportunity for young translation students and graduates to put their skills to the test. This paper focuses on the translation competences activated by the contest, as defined by the Competence Framework produced by the European Masters in Translation Network. It also discusses the potential of this initiative as a motivational tool for translation students and graduates, concluding that individual contests counterbalance the predominance of collaborative activities in the classroom and are therefore a relevant complement to academic training.

NMT verb rendering: A cognitive approach to informing Arabic-into-English post-editing

Abstract Machine translation (MT) has made significant strides and has reached accuracy levels that often make the post-editing (PE) of MT output a viable alternative to manual translation. However, despite professional translators increasingly considering PE as a valid stage in their translation workflow, little has been done to investigate MT output for the purpose of informing training in PE. Against this background, the present project focuses on the handling of tense and aspect configurations in the English translation of Arabic sentences using current neural machine translation (NMT) systems. Using a dataset of representative Arabic sentences, the output of five NMT engines was assessed against reference translations. The investigation reveals regressing accuracy levels when comparing morphological, structural, and contextual tenses. These findings are believed to represent valuable information that contributes to a more informed training in the PE of Arabic-into-English NMT output.

Graph Neural Networks for Charged Particle Tracking on FPGAs.

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph—nodes represent hits, while edges represent possible track segments—and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

ICT-Based Traslation Strategies of Prospective Scientific and Technical Translators

Introduction. Responding to the translation service market requirements scientific and technical translators should be able to choose and properly use ICT-based translation strategies performing a translation workflow in the ICT environment. The purpose of this paper is to specify and systematize ICT-based translation strategies used by practicing translators in the real-life professional setting, which prospective scientific and technical translators should acquire in the process of training. The author applies theoretical (analysis of standards, academic papers and practicing translators’ publications, synthesis of the obtained findings) and empirical (interviewing practicing translators) methods. Discussion. The process of teaching scientific and technical translation correlates with the stages of practicing translators’ professional activity comprising a set of steps aimed at solving particular translation tasks: preparatory, analytical, synthesizing, correction and assessment ones. Practicing translators turn to ICT at most steps of the translation process applying both general-purpose and translation ICT tools. They need to use a global ICT-based translation strategy – an algorithm of translator’s actions aimed at creating the target text, which is determined by ICT tools and the degree of their involvement. The paper highlights the three groups of ICT-based translation strategies (MT-based, TM-based and mixed ones) among which the translator chooses the optimal one at the analytical stage for its application at further steps. This choice is determined by the objective and subjective factors described in the article. Conclusions. In the process of prospective scientific and technical translators’ training special attention should be paid to raising their awareness of possible ICT-based translation strategies and developing appropriate skills.