The Bloomsbury Companion to Language Industry Studies

Erik Angelone, Maureen Ehrensberger-Dow, Gary Massey, Erik Angelone, Maureen Ehrensberger-Dow, Gary Massey

• Publisher: Bloomsbury Academic
• Year: 2019
• Print ISBN: 9781350247109
• eBook ISBN: 9781350024946
• Edition: 1
• Topic: Languages & Linguistics
• Subtopic: Sociolinguistics
• Index: Languages & Linguistics

This volume provides a comprehensive overview of the key issues shaping the language industry, including translation, interpreting, machine translation, editing, terminology management, technology and accessibility. By exploring current and future research topics and methods, the Companion addresses language industry stakeholders, researchers, trainers and working professionals who are keen to know more about the dynamics of the language industry. Providing systematic coverage of a diverse range of translation and interpreting related topics and featuring an A to Z of key terms, The Bloomsbury Companion to Language Industry Studies examines how industry trends and technological advancement can optimize best practices in multilingual communication, language industry workspaces and training.

1

Introduction

Erik Angelone, Maureen Ehrensberger-Dow and Gary Massey

1. Defining and describing the language industry

Today’s language industry is driven by an expanding range of branches that all share some facet of multilingual communication as a common thread. GALA, the Globalization and Localization Association, makes use of the GILT acronym in defining the language industry as one that encompasses the following key services: globalization (G11n), internationalization (I18n), localization (L10n) and translation (T9n).¹ In this volume, we embrace this definition, while also regarding interpreting, consulting, project management and tool design as fundamental pillars. In addition to these services, the language industry can also be defined and described from the perspective of the various stakeholders involved in the day-to-day operations of project lifecycles. These include language service providers (LSPs), vendors, project managers, terminologists, translators, interpreters, revisers, quality assurance specialists and consultants. This is by no means an exhaustive list. The language industry consists of many moving parts. The constellation of these parts tends to vary from one task to the next, which makes sound project management all the more important.

Indeed, the type and scope of projects we see in the language industry on a daily basis are manifold, with varying degrees of logistical complexity. Perhaps a given company plans on launching its operations internationally, in which case large-scale translation and localization services would be at the fore, as would the work of terminologists to ensure consistency. A second type of language industry project might take place in the context of some sort of emergency or crisis situation involving low-resource language pairs. In such an event, real-time cross-language and cross-cultural communication, be it in the form of interpreting services or machine-translation output, is of utmost importance. Yet other projects might call for multilanguage text production from the start, in which case translation and interpreting services would not be implemented as an add-on. Instead, language industry professionals would serve in a capacity of content planning and co-creation. This is where language and cross-cultural consulting plays a pivotal role. Needless to say, the working conditions and environments of language industry professionals (and non-professionals) are far from uniform. In addition to a continued need for routinized experts, who work consistently within a given language industry niche, there is a growing need for adaptive experts, who wear multiple hats by taking on varying roles across niches (see Chapter 8, this volume).

2. Proliferation of the language industry

Language industry activity, as explored in this volume, has a direct or indirect tie-in with translation and/or interpreting as foundational pillars. The manner in which both translation and interpreting are conceptualized and practised highlight an ongoing sea change. Globalization, technological advancement and big data have radically altered the language industry landscape, resulting in a seemingly perpetual state of both flux and growth. For example, in 2017 alone, 265 new LSPs launched operations in the United Kingdom, yielding a total of close to 2,000 at the time of writing (Bond 2018a). According to the U.S. Department of Labor Occupational Outlook Handbook, the job outlook for interpreters and translators is expected to grow by 18 per cent from 2016 through 2026, which is significantly higher than the average of 7 per cent for all occupations in the aggregate.²

We are also witnessing a tremendous proliferation of job titles in the language industry, and in the corresponding roles and responsibilities of language industry professionals (see Chapters 4, 5 and 6, this volume). A recent Slator study on language industry job titles appearing on the LinkedIn networking platform revealed upwards of 600 unique titles (Bond 2018b). Many of these titles, such as specialized translator, conference interpreter, localizer, post-editor and reviser, have been firmly in place for quite some time as established career paths in the language industry. Others, such as transcreator, quality assurance engineer, multilanguage UX designer and strategy consultant, are relatively new on the scene. This plethora of career fields has a direct impact on how we can best go about preparing students for future careers as successful language industry professionals (see Chapter 9, this volume), and such career profiles, though at times seemingly vague and redundant, should serve as a litmus for evaluating curricula. Interestingly, situations involving crisis translation and interpreting services (Federici 2016; O’Brien et al. 2018), a growing need for mediation involving low-resource languages and a shortage of professionally trained specialists have given rise to the somewhat perplex job profile of non-professional interpreter and translator (see Chapter 6, this volume).

This proliferation is in direct correlation with the now more widespread incorporation of machine translation in project workflows (see Chapter 14, this volume), and is in response to both its success and its shortcomings. Indeed, advances in the efficacy of machine translation and artificial intelligence are shaping the language industry at large to an unprecedented extent that extends well beyond the boundaries of written translation. Neural machine translation, in particular, is now widely regarded as game-changing, and artificial intelligence, according to the now seminal New York Times article, as a ‘great awakening’ (Lewis-Kraus 2016). Technological advancement (see Chapter 13, this volume) has changed the landscape in terms of workflow processes and settings (see Chapter 16, this volume), quality and productivity as performance metrics, and the very agents involved in content creation (see Chapter 3, this volume).

3. The ripple effect of proliferation

The increasing integration of machine translation into the workflow of translation projects has resulted in an era of augmented translation, which taps into, and ideally optimizes, the complimentary skill sets of human translators and MT engines.³ Adaptive machine translation is now a common feature in many CAT tool interfaces, to the extent that post-editing of content already in the target language has made ‘from-scratch’ translation increasingly obsolete. Given this paradigm shift in the direction of post-editing of content generated from multiple sources, Pym advocates for the use of the term ‘start text’ in place of the now more dated term ‘source text’ (2013).

In recent years, computer-aided interpreting (CAI) (Fantinuouli 2017) has garnered attention as augmentation for the human interpreter. Not unlike interactive translation platforms based on an array of cognitive process indicators (see Chapter 11, this volume), CAI, ideally, only activates in instances where the interpreter seems to be struggling, as rendered, for example, through extensive pauses and a variety speech disfluencies. The verdict is still out on the extent to which such applications enhance or disrupt the work of interpreters and translators, and more empirical research along these lines in greatly needed. As far as translators are concerned, Common Sense Advisory predicts a trend in which translation memory systems will become more streamlined in line with actual usage patterns (Sargent 2018). Usability studies would be conducive in empirically documenting an optimal interface and constellation of features, as opposed to one that is full of unused or misplaced features that run the risk of triggering cognitive friction (Ehrensberger-Dow and O’Brien 2015).

More robust artificial intelligence applications are also predicted to make project workflows more agile from a project management perspective (Pielmeier 2017), where some level of automation can optimize the components of project intake, vendor management, production and post-processing. Empirical research on the impact of machine translation and artificial intelligence on translation and interpreting is still in its infancy. This is even more the case when it comes to research on its impact in domains such as terminology management (see Chapter 12, this volume), pre- and post-editing (see Chapter 15, this volume) and project management.

The integration of machine translation and artificial intelligence has also ushered in change as far as metrics, assessment and expectations of quality are concerned. It goes without saying that quality is not, and has never been, one dimensional in a one-size-fits-all fashion. Machine translation has made this all the more evident. The automation it enables (with greater or lesser success) has resulted in productivity emerging as a core metric of quality, and the notion of a ‘good enough’ translation has gained visibility. In definitions of fit-for-purpose translation, perishability of content has become an important parameter, alongside parameters such as audience and use. The 2015 implementation of ISO 17100,⁴ with its emphasis on a mandatory stage of revision, can be regarded as a further attempt to ensure quality of MT (and HT) output. The onus shifts from the translator to the reviser(s) (see Chapter 15, this volume), giving way to different constellations of quality assurance within the lifecycle of the translation project, potentially including iterative involvement of end users and clients (see Chapter 7, this volume). In an attempt to assess the quality of both MT and human translation from both process and product perspectives, based on both internal and external metrics, Translation Automation User Society (TAUS) has introduced an automated means by which to gauge performance with its Quality Dashboard.⁵ This, too, introduces new avenues of language industry research not only on how quality can be assessed from a benchmark standpoint, but also on how various industry stakeholders make use of such resources.

Finally, proliferation within the language industry has brought about a shift of focus in the direction of audiences that have often been largely overlooked in the past. For example, media accessibility (see Chapter 11, this volume) has generated significant interest as of late, as both an industry practice and research focal point. Emerging fields, such as audio description, respeaking and speech-to-text reporting,⁶ illustrate the momentum of barrier-free communication in today’s language industry. Web Content Accessibility Guidelines (WCAG) 2.0,⁷ along with the implementation of various national accessibility standards, such as the recently updated Electronic and Information Accessibility Standards ⁸ in the United States, have catalysed work in this area.

The efforts of non-profit organizations, such as Translators without Borders (TWB),⁹ have brought about positive change for audiences in low-resource languages. For example, in its 2017 annual report, TWB provides a snapshot of its efforts in response to the European refugee crisis. Thanks to the work of its largely volunteer team, TWB generated more than 800,000 words of content in Arabic, Kurdish, Urdu, Pashto and Greek, which was disseminated through mobile apps, interpreters and paper signs.¹⁰ These efforts, along with those of others on a daily basis in situations involving crises of various kinds, highlight the important role of so-called non-professional interpreters and translators (see Chapter 6, this volume) as change agents in efforts to ensure societal well-bei ng.

4. Researching the language industry

As suggested by this volume’s title, The Bloomsbury Companion to Language Industry Studies, we are advocating for a deliberate broadening of scope beyond translation and interpreting studies in research endeavours pertaining to today’s multifaceted language industry. We regard language industry studies as a more appropriate term for describing activity in some of the sub-fields mentioned in this introductory chapter, which tend to be regarded as peripheral from a strictly translation studies lens. This volume takes a decidedly broad-brush approach to defining and describing the language industry. It represents an initial attempt to bring together various strands of research (see Chapter 2, this volume) on various fields under one roof, which we are calling language industry studies. Each chapter establishes mutually beneficial synergies between academic research, both present and future, and industry realities.

Core research questions and methods appropriate for language industry studies are presented and explained by Chris Mellinger in Chapter 2. He discusses the importance of understanding the role of stakeholders, transdisciplinarity and the fragmented nature of the industry before embarking on research in this area. The advantages and limitations of experimental, quasi-experimental and observational methods are outlined, with examples from industry research provided as illustration. Since for logistical reasons truly controlled experimental research is relatively rare in the field, Mellinger cautions about making overly strong claims on the basis of other types of studies. The section on data types, analysis and interpretation, and especially the section on ethical considerations, are useful overviews both for newcomers to the area of transdisciplinary and field research and for more experienced researchers. Mellinger is realistic about the challenges of doing language industry research, but optimistic that involvement with industry stakeholders is worth the effort. The three main challenges, in his view, are defining the scope of the industry, gaining access to data and generalizing from results. Critical reflection of methodology and careful choices in the specific areas of research in the language industry, as described in the following chapters, help address these and other challenges.

In Chapter 3, Hanna Risku, Regina Rogl and Jelena Milošević explore research methods, tools and approaches as they apply to language industry workplace studies, a focal point that is still in its infancy. In broad term...

Table of contents

• Cover
• Half-Title
• Series
• Title
• Contents
• List of contributors
• Foreword
• 1 Introduction
• 2 Core research questions and methods
• 3 Researching workplaces
• 4 Translators’ roles and responsibilities
• 5 Interpreters’ roles and responsibilities
• 6 Non-professional interpreting and translation (NPIT)
• 7 Tailoring translation services for clients and users
• 8 Professional translator development from an expertise perspective
• 9 Training and pedagogical implications
• 10 Audiovisual translation
• 11 Audiovisual media accessibility
• 12 Terminology management
• 13 Translation technology – past, present and future
• 14 Machine translation: Where are we at today?
• 15 Pre-editing and post-editing
• 16 Advances in interactive translation technology
• A–Z key terms and concepts
• Index
• Copyright