Transl-AI-tion 2.0
Embracing the AI Revolution
Summary
This edited volume brings together scholars and practitioners from around the world to examine the technological, ethical, legal and pedagogical dimensions of this transformation. It explores developments such as neural machine translation, large language models, wearable translation devices and AI-assisted subtitling, while critically examining algorithmic bias, copyright challenges in the age of generative AI, questions of accessibility and linguistic justice, and the evolving roles of human translators. The various chapters evaluate real-world AI tools; investigate quality and professional practice, particularly in audiovisual translation; and consider how translator education must adapt to technological change.
Rather than seeing AI as a substitute for human expertise, this volume advocates a responsible, human-centred integration that safeguards the cultural, ethical and interpretive dimensions of translation while harnessing technological potential.
Excerpt
Table Of Contents
- Cover
- Title Page
- Copyright Page
- Dedication
- Contents
- List of Figures
- List of Tables
- Foreword (Josélia Neves)
- Acknowledgements
- List of Abbreviations
- Introduction
- Part I: Foundations and Ethical Frontiers of AI in Translation
- Part II: Innovations and Challenges in AI-driven Translation Tools
- Part III: Audiovisual Translation in the Age of AI
- Part IV: Pedagogy and the Future of Translator Education
- Part I Foundations and Ethical Frontiers of AI in Translation
- Chapter 1 The Future of AI in Translation: Balancing Innovation with Ethics (Rashid Yahiaoui)
- Introduction
- AI Innovations in Translation and Interpreting
- Generative AI in Translation
- AI in Interpreting
- AI in Audiovisual Translation & Ethical Concerns
- Multimodal AI and Human–AI Collaboration
- Policy and Regulation
- Conclusion
- References
- Chapter 2 Translation Copyright in the Age of Generative AI: Addressing Legal and Ethical Challenges (Katharina Walter)
- Introduction
- Translation Copyright Conceived: A Brief Historical Overview
- Translation Copyright Unpacked: Changing Conceptions of Textual Identity and Authorship Across History
- Translation Copyright Revisited: Potential Responses to Ethical and Legal Challenges Caused by the AI Revolution
- Outlook: Towards a Sustainable Conception of Copyright in and outside Translation
- References
- Chapter 3 Large Language Models as Translators: Comparing ChatGPT and Google’s Neural Translation Model to the Preservation of the Author’s Stylometric Profile (George Mikros)
- Introduction
- Literature Review
- Neural Translation
- Stylometry in Translation
- Methodology
- Corpus Selection
- Translation Process
- Machine Translation Evaluation
- Stylometric Analysis
- Results
- Stylometric Profiles of Source Texts
- Translation quality comparison using GPT-4 as evaluator
- Using authorship attribution accuracy as an indirect translation evaluation measure
- Conclusion
- References
- Part II Innovations and Challenges in AI-driven Translation Tools
- Chapter 4 Wearable Translation Technologies: Current State and Future Prospects (Hussein Abu-Rayyash and Naif Alanazi)
- Introduction
- Theoretical Foundations and Epistemological Considerations
- Current State of WTTs
- Form Factors and Capabilities
- Breakthrough in Sign Language Translation
- Recent Developments and Platforms in the Wearables Market
- Technical Implementation and User Experience
- Role, Status, and Professional Identity in the Context of WTTs
- WTTs and Ethics
- Future Prospects
- Conclusion
- Acknowledgement
- References
- Chapter 5 Assessing Diacritics in Arabic Machine and Human Translations: Kanafani’s “A Pithy Maxim” as an Example (Ruqaya Sabeeh Al-Taie)
- Introduction
- Importance of diacritics in Arabic
- Pre-editing and/or post-editing diacritics
- MT of diacritics: Data analysis and discussion
- Conclusion and Recommendation
- References
- Chapter 6 AI-based Assessment of Intralingual Live Subtitles (Luis Alonso-Bacigalupe and Oscar Alonso-Amigo)
- Introduction
- Quality Issues in Translation & Interpreting
- Media Accessibility, Intralingual and Interlingual Live Subtitling, and Respeaking
- Quality Assessment in T&I
- Quality Assessment in ILS: The WER and NER Models
- Technology and language processing
- Machine Learning and Large Language Models
- Human language processing and AI: Common ground
- Training LLMs
- A Few Thoughts on Prompting and Fine-tuning
- NER Buddy: An application for the AI-based Assessment of ILS
- Testing Two LLMs: Main Indicators
- Results with GPT-3.5
- Results With Open-source Models: OpenChat
- The Road Ahead
- Conclusion
- Acknowledgements
- References
- Part III Audiovisual Translation in the Age of AI
- Chapter 7 The Impact of AI on Subtitling: State of the Art (Kaouthar Rahmani and Abdennour Kharraki)
- Introduction
- Assessing the Current Digital Subtitling Landscape
- Artificial Intelligence and Subtitling
- The Shifting Role of Subtitlers in the Era of AI
- Subtitling Quality Assessment in the Era of AI
- Concluding Remarks
- References
- Chapter 8 Automatically Generated Intralingual and Interlingual Subtitling and the Need for Human Intervention (Ilaria Parini)
- Introduction: Technological Advancements in Subtitling
- Historical Development of Audiovisual Machine Translation
- Applications of ASR Technologies: The Case of YouTube
- A Practice-based Study
- Methodology
- Analysis of intralingual subtitles
- Formal requirements (lack of punctuation, timing, and line breaks)
- Misrecognition of words
- Homophones and polysemes
- Misrecognition of entity names
- Analysis of Italian Interlingual Subtitles
- Formal requirements (lack of punctuation, timing, line breaks)
- Misrecognition of words
- Homophones and polysemes
- Misrecognition of entity names
- Conclusion
- References
- Chapter 9 Machine-Translated Subtitles and Audiovisual Translation: A Perfect Match? Investigating Quality Through the Consumer’s Lens (Frederike Schierl)
- Introduction
- Quality – Some Definitions
- Quality in general
- Quality in the translation industry
- Quality in audiovisual translation – the case of subtitling
- Measuring quality
- Automatic evaluation of machine translation output
- Automatic evaluation without human intervention
- Quality evaluation through human assessment
- Subtitling and machine translation
- Quality Perception Through the Consumer’s Lens: Results of a Reception Study
- Experiment Setup and Data Analysis
- Preliminary Results
- Perception of machine-translated subtitles
- Discussion
- Conclusion
- References
- Part IV Pedagogy and the Future of Translator Education
- Chapter 10 AI Unveiled: Mapping the Integration of AI in Translator and Interpreter Education (Susana Álvarez-Álvarez)
- Introduction
- Technology and AI-based tools in T&I training
- Technology and AI-based tools in the professional market: Navigating an evolving landscape
- Technology and AI-based tools in the T&I curriculum in Spain: A general overview
- Technology and AI integration in T&I training and professional practice: Perceptions and attitudes of the main stakeholders
- Undergraduate students’ perceptions and attitudes
- Graduates’ perceptions and attitudes
- Employers’ and professional translators’ perceptions and attitudes
- Conclusion
- References
- Chapter 11 Investigating Students’ Attitudes towards the Use of AI in Interlingual Subtitling Process (Marco Agnetta)
- Introduction
- Literature Review
- Machine Translation and Subtitling
- The Experiment
- Results
- Attitudes Towards AI-Based Subtitling
- Temporal effort
- Limitations and Replicability of the Experiment
- Conclusion and Outlook
- References
- Chapter 12 Audiovisual Translation Technologies: Training the Next Generation (Verónica Arnáiz-Uzquiza and Paula Igareda)
- Audiovisual Translation and Media Accessibility in Translation Studies
- Current Training Practices in AVT and MA across Europe
- Formal Training
- Non-Formal Training
- MOOCs
- Micro-credentials
- Private training
- Certifications
- Summer Courses
- Technological Tools and Resources in AVT and MA Training
- Computer-Assisted Translation tools and Machine Translation and Post Editing
- Speech Recognition and Synthesis Systems
- AVT “Translation-Only” Tools
- Other Tools, Apps, and Resources
- Future Directions in AVT and MA Training
- Conclusion
- References
- Notes on Contributors
- Index
Figures
Figure 3.1: Hierarchical Cluster Analysis of the source language novels chunks (5,000 words each) using 1,000 most frequent words and the Delta distances
Figure 3.2: Hierarchical Cluster Analysis of the translated language novels chunks (5,000 words each) using 1,000 most frequent words and the Delta distances. Includes both GPT-4 and Google Translate
Figure 3.3: Interaction plot of the two-way ANOVA
Figure 6.1: Large language models
Figure 6.2: The tokenisation problem
Figure 6.3: Automatic identification of errors with NER Buddy
Figure 6.4: Full details of automatic assessment with NER Buddy
Figure 7.1: MT paradigms in audiovisual translation
Figure 7.2: The subtitling process in the era of machine translation
Figure 7.3: Subtitler’s dynamic competences in the era of AI
Figure 9.1: Features of quality according to the participants’ perception
Figure 10.1: T&I undergraduate students per university participating in the survey
Figure 10.2: AI content missing according to undergraduate students
Figure 10.3: AI tools most frequently used by T&I undergraduate students
Figure 10.4: Level of usage of AI-based tools by T&I undergraduate students
Figure 10.5: Primary purposes for using AI tools in studies according to T&I undergraduate students
Figure 10.6: Translation-related jobs undertaken by T&I graduates
Figure 10.7: AI content missing according to T&I graduates
Figure 10.8: AI tools most frequently used by T&I graduates
Figure 10.9: Level of usage of AI-based tools by T&I graduates
Figure 10.10: Primary purposes for using AI tools according to T&I graduates
Figure 10.11: AI content missing according to employers and professional translators
Figure 10.12: AI tools most frequently used by employers and professional translators
Figure 10.13: Level of usage of AI-based tools by employers and professional translators
Figure 10.14: Primary purposes for using AI tools according to employers and professional translators
Figure 12.1: CAT, MT and PE tools included in degree courses in Spain (according to their syllabi)
Figure 12.2: AVT courses in the 94 programmes of the EMT network
Tables
Table 3.1: Basic descriptive statistics of the corpus
Table 3.2: Classification evaluation metrics in the Authorship Attribution task using the Random Forest algorithm with three different feature groups (text complexity and readability, author multilevel N-gram profile, and word embeddings)
Table 4.1: Examples of Commercially Available WTTs
Table 5.1: Reverso and Google translations of دُفِعَ without diacritics
Table 5.2: Reverso and Google translations of دُفِعَ with diacritics
Table 5.3: Reverso and Google translations of عُرِفَ without diacritics
Table 5.4: Reverso and Google translations of عُرِفَ with diacritics
Table 5.5: Reverso and Google translations of قَبَضَ without diacritics
Table 5.6: Reverso and Google translations of قَبَضَ with diacritics
Table 5.7: Reverso and Google translations of سَمَعَ without diacritics
Table 5.8: Reverso and Google translations of سَمَعَ with diacritics
Table 5.9: Reverso and Google translations of عَدها without diacritics
Table 5.10: Reverso and Google translations of عَدها with diacritics
Table 6.1: Efficiency of five live interlingual subtitling workflows
Table 6.2: Comparison of ASR software
Table 6.3: Results of assessment. Human vs. automatic ChatGPT
Table 6.4: Confusion matrix with number and severity of errors. ChatGPT vs. human
Table 6.5: Efficiency of open-source LLMs
Table 6.6: Results of assessment. Human vs automatic OpenChat
Table 6.7: Confusion matrix with number and severity of errors. OpenChat vs human
Table 8.1: Example of a passage with automatic intralingual subtitles and its post-edited version, focusing on fixing the lack of conformity regarding formal requirements
Table 8.2: Example of a passage containing misrecognition of common words in the intralingual subtitles and its post-edited version
Table 8.3: Example of a passage containing misrecognition of common words in the intralingual subtitles and its post-edited version
Table 8.4: Examples of passages containing misrecognition of common words in the intralingual subtitles and their post-edited versions
Table 8.5: Example of a passage containing misrecognition of common words in the intralingual subtitles and its post-edited version
Table 8.6: Example of a passage containing misrecognition of entity words in the intralingual subtitles and its post-edited version
Table 8.7: Example of a passage with automatic intralingual subtitles which do not conform with professional formal requirements and subsequent cascading errors in the interlingual subtitles
Table 8.8: Example of a passage containing misrecognition of common words in the intralingual subtitles, which leads to cascading errors in the interlingual subtitles and their post-edited version
Table 8.9: Example of a passage containing misrecognition of common words in the intralingual subtitles, which leads to cascading errors in the interlingual subtitles and their post-edited version
Table 8.10: Examples of passages containing misrecognition of common words in the intralingual subtitles, which leads to cascading errors in the interlingual subtitles, and their post-edited version
Table 8.11: Example of a passage containing misrecognition of common words in the intralingual subtitles, which leads to cascading errors in the interlingual subtitles and their post-edited version
Table 8.12: Example of a passage containing misrecognition of entity words in the intralingual subtitles, which leads to cascading errors in the interlingual subtitles and their post-edited version
Table 9.1: Quantitative results of the annotation procedure of the aspect “quality”
Table 9.2: Quantitative results of the annotation procedure for the perception of machine translation
Table 11.1: Participation in the experiment by language pair, direction of translation and division into group A (without AI) and group B (with AI)
Table 11.2: Absolute, relative editing times and translation experience in Group A
Table 11.3: Absolute, relative editing times and translation experience in Group B
Foreword
I feel honoured to be writing this foreword to Transl-AI-tion 2.0: Embracing the AI Revolution, edited by Rashid Yahiaoui, with a collection of papers by scholars I respect and admire. As I read the volume, I can only reflect on the long journey that has brought us to this moment in which our beliefs and practices are being questioned. Having lived through the advent of the computer and then the internet, I look back to ask how knowledge was produced and disseminated before these technologies emerged. Here, I stop anew, only to look forward, finding it difficult to imagine how reality will be reshaping itself through the impact of artificial intelligence (AI).
This volume couldn’t have come at a better time. It finds us, researchers, teachers, and professionals of translation, grappling with challenges that are technological, methodological, pedagogical, and, ultimately, ethical. We are all aware of the opportunities and dangers that come with rapid technological advancements, and we are all facing the anguish of having to deal with fake news, the silencing of voices that do not align with specific economic and political interests, and the intensification of fear and inequality.
If we are reading this volume, we will share a common interest in communication and be aware of the value of translation bridging between people and cultures while removing barriers that may be inherent to individuals’ biology, unsustainable policies and practices, or simply driven by social biases. We will likely be seeking answers to questions such as “How can we embrace AI while keeping intact what makes us human(e)?”
I believe this publication provides us with valuable insights that may bring us closer to finding an answer to the question above. As we progress through the various chapters, we will be introduced to engaging discussions about cutting-edge technologies, including large language models, smart wearable devices, and automated translation systems, among others. We will see how translation practices have benefited from automation and how audiovisual media have leveraged their output to capitalise on the latest technological developments in language processing and image and sound manipulation. We are also given the opportunity to read about how translation practices have followed suit as they adopt AI-driven solutions that enable faster turnaround and greater efficiency. Others will address the ethical implications of algorithmic bias or the affordances of generative AI. But the chapters that have captured my special interest are those that allow us to reflect on the new roles that all those working on translation will soon be embracing. We are invited to anticipate educational shifts that will prepare professionals for a future that is largely unknown and to recognise that translators must rapidly upskill if they are to remain in the field.
This volume extends beyond reflections on technology and practice to highlight relevant concerns regarding truth, fairness, transparency, and the responsible use of technology. It also highlights the importance of adaptability, creativity, critical thinking, empathy, and cultural sensitivity – qualities that cannot be easily replicated by machines. In so doing, there is an implicit call for action to all who have the power to “change the world”, particularly those developing the tools of the future and those in education. It is also a call for thoughtful engagement, responsible development, and a commitment to ensuring that the benefits of AI are shared by all. In short, it is a call for educators to adapt their curricula, for practitioners to embrace new tools, and for policymakers to establish ethical guidelines that protect the rights of both creators and consumers.
I am confident that you will find this volume to be a stimulating and enlightening journey. I hope it challenges you, as it does me, to think critically about the future of translation and inspires you to continue your work in promoting responsible innovation and inclusivity in this vital field. As you read through this volume, be prepared to be challenged and be open to new reflections. The honesty and generosity with which all topics are addressed allow us to believe that, collectively, we can grow into informed, critical users of AI as a tool and partner, co-creating communication mediation services that will have a real and positive impact on people’s lives.
Enjoy the read!
Details
- Pages
- XXX, 290
- Publication Year
- 2026
- ISBN (PDF)
- 9781803743400
- ISBN (ePUB)
- 9781803743417
- ISBN (Softcover)
- 9781803743394
- DOI
- 10.3726/b21402
- Language
- English
- Publication date
- 2026 (October)
- Keywords
- Artificial intelligence in translation neural machine translation audiovisual translation translation ethics translator education accessibility machine-translation post editing
- Published
- Oxford, Berlin, Bruxelles, Chennai, Lausanne, New York, 2026. xxx, 290 pp., 27 fig. b/w, 37 tables.
- Product Safety
- Peter Lang Group AG
