ISSN: 1579-9794
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KENNY, DOROTHY (ED.). MACHINE TRANSLATION FOR EVERYONE:
EMPOWERING USERS IN THE AGE OF ARTIFICIAL INTELLIGENCE.
BERLIN, LANGUAGE SCIENCE PRESS, 2022, 210 PP., ISBN 978-3-
98554-045-7
The emergence of machine translation (MT) as an increasingly relevant
subject in Translation Studies has been driven by its substantial and profound
implications for the language services industry. Dorothy Kenny’s Machine
Translation for Everyone: Empowering Users in the Age of Artificial
Intelligence situates MT within this dynamic and explores MT’s potential to
democratise multilingual communication. This comprehensive volume, part of
the Translation and Multilingual Natural Language Processing series by
Language Science Press, is a key resource for both translation students,
scholars and practitioners. Funded by the Erasmus+ project “MultiTraiNMT:
Machine Translation Training for Multilingual Citizens,” the book serves as a
primer and a detailed exploration of MT's possibilities and limitations.
Machine Translation for everyone includes different chapters by
renowned Translation Studies scholars and addresses MT’s integration into
professional and personal workflows. As noted by Bowker & Ciro (2019), MT
literacy is crucial for informed MT usage, as it empowers people to critically
engage with MT systems, understanding their strengths, limitations, and
ethical implications. Reading and understanding Machine Translation for
Everyone represents a significant step towards fostering this MT literacy,
which is very much required today, equipping readers with the tools and
knowledge needed to utilise MT effectively. Kenny and colleagues extend this
argument by emphasizing MT’s ethical, technical, and practical dimensions.
This book builds on previous explorations, such as those of Moorkens et al.,
(2018) on translation quality evaluation and Forcada's (2017) work on MT
architectures, to provide a well-rounded perspective tailored to a wide
audience.
The book provides a structured journey through the landscape of MT,
starting with its societal and multilingual implications and moving toward more
technical and applied dimensions. Early chapters (1-6) contextualize MT in
today’s society and introduce readers to MT within the context of European
multilingualism, offering insights into what MT is, its potential uses, and the
considerations surrounding quality, ethics, and effective interaction with MT
output. More specifically, Chapter 1 focuses on the foundational role of
multilingualism in European policy and how MT can support this objective.
Chapter 2 dispels myths about translation by addressing common
misconceptions about the nature of translation and MT, and it provides
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guidance on the key concepts of MT, including foundational principles and
how MT systems interact with linguistic structures. This chapter sets the stage
for a better understanding of the complexities involved in human and machine-
mediated translation. Chapter 3 introduces methodologies for evaluating MT
output, emphasizing usability and fitness-for-purpose, and highlighting the
multiple issues of assessing translation “quality”. Chapter 4 discusses pre-
editing strategies for improving MT input, particularly for global audiences,
highlighting methods such as simplifying syntax, avoiding ambiguous
expressions, and ensuring cultural neutrality to optimize MT outcomes.
Chapter 5 delves into the practice of post-editing, emphasizing its importance
in refining machine-generated outputs by correcting errors, improving fluency,
and ensuring the translation meets the intended quality standards, which is
particularly critical for professional and high-stakes use cases. Finally,
Chapter 6 addresses the ethical dimensions of MT, exploring issues like data
privacy, bias, and environmental concerns, urging readers to adopt a reflective
and responsible approach to technology use. In the latter part of the book,
technical foundations of MT are then explained in an accessible manner, even
for those who may not be very acquainted with language technologies and
computing. Chapter 7 delves into neural machine translation (NMT), providing
a comprehensible explanation of its workings tailored to non-technical
readers, enhanced by practical examples and diagrams that break down
complex concepts such as contextual embeddings. Chapter 8 further explores
customisation and self-training of NMT systems, offering a detailed, step-by-
step guide for users aiming to tailor these technologies to specific needs,
highlighting the potential for application across various domains and settings.
Chapter 9 takes a more applied approach, showcasing how MT can be
effectively integrated into language learning contexts. It explores practical
strategies for using MT tools to enhance reading comprehension, foster critical
thinking, and support the acquisition of writing skills in a second language.
Personally, what I consider the standout feature of the book is its
accompanying materials: First, a collection of self-paced activities, including
fill-in-the-blank exercises and multiple-choice questions (among other type of
assignments), designed to challenge both students and educators in
translation studies. These resources are accessible through a permanent link
and can be very useful for translation trainers:
https://ddd.uab.cat/record/257869. Second, and even more importantly, the
MutNMT platform (https://ntradumatica.uab.cat/), which enables users to train
and customize small NMT systems with features for data selection, corpus
preparation, personalization, and evaluation methodologies. Even if the
resulting NMT system trained will not match the quality of current commercial
systems, the process of engaging with the creation, cleaning, training, and
evaluation of a personal MT system provides invaluable hands-on experience.
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This journey is particularly beneficial for students and researchers, as it allows
for creating a deeper understanding of NMT principles and enhances their
technical and analytical skills in practical settings.
The volume excels in several areas. Its comprehensiveness is evident
in the way it spans theoretical, practical, and ethical dimensions of MT, making
it a complete resource. Despite the technical complexity of the subject matter
in some parts, the book remains accessible, employing clear explanations and
minimal jargon to engage a broad readership (for those stuck with Chapter 7,
understanding technical descriptions of NMT will always require making an
extra effort). Furthermore, the inclusion of interactive activities and the
MutNMT platform bridges the gap between theory and practice, fostering
active learning.
If I were to adopt a more critical perspective, recognising the inherent
difficulty of improving an already comprehensive and thoughtful work, I might
suggest broadening the book’s exploration beyond its rich and detailed focus
on European multilingualism to encompass more global contexts (though the
reviewer can understand that the book originated from an Erasmus+ project
funded by the European Union, and that may be the reason for exclusively
focusing on Europe). Lastly, integrating a more diverse array of real-world
case studies from various industries could significantly enhance the book’s
practical utility. Such additions would provide readers with concrete examples
of MT’s implementation across varied professional environments,
demonstrating its adaptability and addressing specific challenges and
solutions encountered in practice. These refinements, while not essential,
could improve the book’s impact and applicability even further.
Machine Translation for Everyone is a brilliant publication that
underscores the transformative potential of MT while urging critical and ethical
engagement with the technology. I would even say that it is a must-read for
anyone in Translation Studies, from students and educators to industry
professionals, if they really want to learn how NMT works, how to assess NMT
output, and the current limitations of such a technology for translation. The
book’s emphasis on fostering MT literacy resonates with current trends toward
hybrid translator profiles, such as “language engineers(Briva-Iglesias &
O’Brien, 2022) and “MT literacy consultants” (Ehrensberger-Dow et al., 2023).
As the industry evolves, this book provides a vital foundation for navigating
the challenges and opportunities presented by MT and current AI-powered
translation systems. As a final note, it is worth stressing that, although the title
of the book references "Artificial Intelligence," in the context of this volume, it
specifically refers to the NMT paradigm, which is a subset of AI. By the time
of the book's writing and release, more recent AI-powered translation tools,
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such as large language models (LLMs), were still emerging and had not yet
gained widespread prominence in the field.
REFERENCES
Bowker, L., & Ciro, J. B. (2019). Machine translation and global research:
Towards improved machine translation literacy in the scholarly
community. Emerald Publishing Limited.
https://www.emerald.com/insight/content/doi/10.1108/978-1-78756-
721-420191009/full/html
Briva-Iglesias, V., & O’Brien, S. (2022). The Language Engineer: A
Transversal, Emerging Role for the Automation Age. Quaderns de
Filologia - Estudis Lingüístics, 27(0), Article 0.
https://doi.org/10.7203/qf.0.24622
Ehrensberger-Dow, M., Delorme Benites, A., & Lehr, C. (2023). A new role for
translators and trainers: MT literacy consultants. The Interpreter and
Translator Trainer, 17(3), 393411.
https://doi.org/10.1080/1750399X.2023.2237328
Forcada, M. L. (2017). Making sense of neural machine translation.
Translation Spaces, 6(2), Article 2. https://doi.org/10.1075/ts.6.2.06for
Moorkens, J., Castilho, S., Gaspari, F., & Doherty, S. (Eds.). (2018).
Translation Quality Assessment: From Principles to Practice (Vol. 1).
Springer International Publishing. https://doi.org/10.1007/978-3-319-
91241-7
[VICENT BRIVA-IGLESIAS]