Artificial intelligence is changing and will continue to change the world we live in. These changes are also influencing the translation market. Machine translation (MT) systems automatically transfer one language to another within seconds. However, MT systems are very often still not capable of producing perfect translations. To achieve high quality translations, the MT output first has to be corrected by a professional translator. This procedure is called post-editing (PE). PE has become an established task on the professional translation market. The aim of this text book is to provide basic knowledge about the most relevant topics in professional PE. The text book comprises ten chapters on both theoretical and practical aspects including topics like MT approaches and development, guidelines, integration into CAT tools, risks in PE, data security, practical decisions in the PE process, competences for PE, and new job profiles.

Extraordinary advances in machine translation over the last three quarters of a century have profoundly affected many aspects of the translation profession. The widespread integration of adaptive “artificially intelligent” technologies has radically changed the way many translators think and work. In turn, groundbreaking empirical research has yielded new perspectives on the cognitive basis of the human translation process. Translation is in the throes of radical transition on both professional and academic levels. The game-changing introduction of neural machine translation engines almost a decade ago accelerated these transitions. This volume takes stock of the depth and breadth of resulting developments, highlighting the emerging rivalry of human and machine intelligence. The gathering and analysis of big data is a common thread that has given access to new insights in widely divergent areas, from literary translation to movie subtitling to consecutive interpreting to development of flexible and powerful new cognitive models of translation.

Translation technology is essential for translation students, practising translators, and those working as part of the language services industry, but looming above others are the tools for automating translation: machine translation and, more recently, generative AI based on large language models (LLMs). This book, authored by leading experts, demystifies machine translation, explaining its origins, its training data, how neural machine translation and LLMs work, how to measure their quality, how translators interact with contemporary systems for automating translation, and how readers can build their own machine translation or LLM. In later chapters, the scope of the book expands to look more broadly at translation automation in audiovisual translation and localisation. Importantly, the book also examines the sociotechnical context, focusing on ethics and sustainability. Enhanced with activities, further reading and resource links, including online support material on the Routledge Translation studies portal, this is an essential textbook for students of translation studies, trainee and practising translators, and users of MT and multilingual LLMs.

Language learning and translation have always been complementary pillars of multilingualism in the European Union. Both have been affected by the increasing availability of machine translation (MT): language learners now make use of free online MT to help them both understand and produce texts in a second language, but there are fears that uninformed use of the technology could undermine effective language learning. At the same time, MT is promoted as a technology that will change the face of professional translation, but the technical opacity of contemporary approaches, and the legal and ethical issues they raise, can make the participation of human translators in contemporary MT workflows particularly complicated. Against this background, this book attempts to promote teaching and learning about MT among a broad range of readers, including language learners, language teachers, trainee translators, translation teachers, and professional translators. It presents a rationale for learning about MT, and provides both a basic introduction to contemporary machine-learning based MT, and a more advanced discussion of neural MT. It explores the ethical issues that increased use of MT raises, and provides advice on its application in language learning. It also shows how users can make the most of MT through pre-editing, post-editing and customization of the technology.

Has the language industry of the 21st century been racing ahead of the translation profession and leaving translators behind? Or are translators adapting to new sociotechnical realities and societal demands, and if so, how? The chapters in this volume seek to shed light on the profiles and position of human translators in the current decade. This collection draws together the work of leading authors to reflect on the constantly evolving language industry. The eight chapters present new perspectives on, and concepts of, translation in a digital world. They highlight the shifts taking place in the sociotechnical environment of translation and the need to address changing buyer needs and market demands with new services, profiles and training. In doing so, they share a common focus on the added value that human translators can and do bring to bear as adaptive, creative, digitally literate experts. Addressing an international readership, this volume is of interest to advanced students and researchers in translation and interpreting studies, and professionals in the global language industry.

What does it take to be a professional translator in the 21st century? What are the opportunities and challenges of a career in translation? How do you find that first job? How do you ensure that work remains sustainable over time? Combining industry insights, the latest research in the field of translation studies and a career coaching approach, this textbook takes aspiring translators on an explorative journey that helps them answer these questions for themselves so they can become the professional translators they aspire to be. Each chapter of this hands-on guide opens with key questions that budding translators might typically ask themselves and encourages them to reflect on their relevance for their own situation through regular discussion points and ‘Topics for discussion and assignments’. Targeted suggestions for further reading at the end of each chapter guide users in deepening their knowledge. Written primarily for students on translation courses, the accessible language, tone and design of this book will appeal to anyone who is thinking of embarking upon a career in translation. Additional resources are available on the Routledge Translation Studies Portal.

This textbook provides an accessible introduction to the field of translation for students of other disciplines and readers who are not translators. It provides students outside the translation profession with a greater awareness of, and appreciation for, what goes into translation. Providing readers with tools for their own personal translation-related needs, this book encourages an ethical approach to translation and offers an insight into translation as a possible career. This textbook covers foundational concepts; key figures, groups, and events; tools and resources for non-professional translation tasks; and the types of translation that non-translators are liable to encounter. Each chapter includes practical activities, annotated further reading, and summaries of key points suitable for use in classrooms, online teaching, or self-study. There is also a glossary of key terms. De-mystifying Translation: Introducing Translation to Non-translators is the ideal text for any non-specialist taking a course on translation and for anyone interested in learning more about the field of translation and translation studies.

This concise volume serves as a valuable resource on understanding the integration and impact of generative AI (GenAI) and evolving technologies on translation workflows. As translation technologies continue to evolve rapidly, translation scholars and practicing translators need to address the challenges of how best to factor AI-enhanced tools into their practices and in translator training programs. The book covers a range of AI applications, including AI-powered features within Translation Management Systems, AI-based machine translation, AI-assisted translation, language generation modules and language checking tools. The volume puts the focus on using AI in translation responsibly and effectively, but also on ways to support students and practitioners in their professional development through easing technological anxieties and building digital resilience. This book will be of interest to students, scholars and practitioners in translation and interpreting studies, as well as key stakeholders in the language services industry.

The purpose of this book is to showcase a diverse set of directions in empirical research on mediated discourse, reflecting on the state-of-the-art and the increasing intersection between Corpus-based Interpreting Studies (CBIS) and Corpus-based Translation Studies (CBTS). Undeniably, data from the European Parliament (EP) offer a great opportunity for such research. Not only does the institution provide a sizeable sample of oral debates held at the EP together with their simultaneous interpretations into all languages of the European Union. It also makes available written verbatim reports of the original speeches, which used to be translated. From a methodological perspective, EP materials thus guarantee a great degree of homogeneity, which is particularly valuable in corpus studies, where data comparability is frequently a challenge. In this volume, progress is visible in both CBIS and CBTS. In interpreting, it manifests itself notably in the availability of comprehensive transcription, annotation and alignment systems. In translation, datasets are becoming substantially richer in metadata, which allow for increasingly refined multi-factorial analysis. At the crossroads between the two fields, intermodal investigations bring to the fore what these mediation modes have in common and how they differ. The volume is thus aimed in particular at Interpreting and Translation scholars looking for new descriptive insights and methodological approaches in the investigation of mediated discourse, but it may be also of interest for (corpus) linguists analysing parliamentary discourse in general.

The present work explores computer-assisted simultaneous interpreting (CASI) from a primarily cognitive perspective. Despite concerns over the potentially negative impact of computer-assisted interpreting (CAI) tools on interpreters’ cognitive load (CL), this hypothesis remains untested. Previous research is restricted to the evaluation of the CASI product and a methodology for the process-oriented evaluation of CASI and the empirical evidence for its cognitive modelling are missing. Overcoming these limitations appears essential to advance CAI research, particularly to foster a deeper understanding of the cognitive aspects of CAI through a validated research methodology and to determine the feasibility of the integration of CAI tools into the interpreting process. This book tests and validates a methodology for the combined exploration of the product and process of CASI. Quantitative and qualitative data were collected during an eyetracking experiment at the Translation & Cognition Centre of the University of Mainz. The study followed a convergent mixed-method and multi-method approach and involved nine interpreting students. Prior to the experimental task, the informants were trained in the use of three terminology support tools: a digital glossary in PDF format, a CAI tool with manual look-up, and a mock-up CAI tool with integrated automatic speech recognition (ASR) for terminology. After several pre-tests, the participants interpreted three speeches from English into their native German using a different tool each time. To increase comparability between the three conditions and control for potentially confounding variables, the speeches were validated during a pilot study. The students’ gaze data and deliveries were recorded and analysed. Qualitative data on the informants’ perception of the tools were collected post-hoc. In the study, several performance, behavioural, and subjective measures were analysed: terminological accuracy and errors and omissions; glossary queries, ear-voice span, inter-cluster pause duration, time to first fixation, average fixation duration, and fixation time; qualitative questionnaire responses. The findings provide insights into the effects of CAI tools on CL and attention allocation in interpreter-CAI tool interaction during simultaneous interpreting. As this is the first study on in-process CAI tool use with a markedly cognitive orientation, it entails significant implications for the methodological development of CAI research and the design of future studies on cognitive aspects of CASI, while raising additional questions in need of further investigation.