International Journal of Communication 10(2016), 947–969 1932–8036/20160005
Copyright © 2016 (Stephen Doherty). Licensed under the Creative Commons Attribution Non-commercial
No Derivatives (by-nc-nd). Available at http://ijoc.org.
The Impact of Translation Technologies
on the Process and Product of Translation
The University of New South Wales, Australia
Technological advances have led to unprecedented changes in translation as a means of
interlingual communication. This article discusses the impact of two major technological
developments of contemporary translation: computer-assisted translation tools and
machine translation. These technologies have increased productivity and quality in
translation, supported international communication, and demonstrated the growing need
for innovative technological solutions to the age-old problem of the language barrier.
However, these tools also represent significant challenges and uncertainties for the
translation profession and the industry. In highlighting the need for increased awareness
and technological competencies, I propose that these challenges can be overcome and
translation technologies will become even more integral in interlingual communication.
Keywords: translation technology, machine translation, international communication,
As a constant in the development of humanity, translation has always played a crucial role in
interlingual communication by allowing for the sharing of knowledge and culture between different
languages. This diffusion of information can be found as far back as the ancient world through to the
industrial age and into the global village of today, where technological advances opaque our perception of
translation and the ascendancy of English as the lingua franca can easily lead us to believe that everything
we know, and indeed everything worth knowing, somehow exists in one language. Much of the wealth of
knowledge and richness of experience that is constructed and documented in our societies is, however,
confined within language silos, to which access is restricted for most of us, even with our favorite Internet
Stephen Doherty: email@example.com
Date submitted: 2014–11–18
My thanks to the reviewers and guest editor for their comprehensive feedback and constructive
948 Stephen Doherty International Journal of Communication 10(2016)
Cronin (2013) argues that any form of global interaction cannot occur without interlingual
activities and thus globalization denotes translation, yet many of us are simply unable or unwilling to
overcome the associated language barrier and must therefore rely on translation provided by others to
access information beyond our own individual linguistic reach. Traditionally, the translator (and
interpreter) has played this role and provided a professional service in acting as an interlingual and
intercultural communicator so that we can access the information we seek, if indeed we knew it existed
there in the first place. Due to its very nature, we typically do not recognize translation even when it is
right before our very eyes (e.g., Kenny, 1996). With the explosion of digital content and the maturing
participatory online culture of Web 2.0 technologies (O’Reilly, 2005), traditional human translation simply
cannot keep up the pace with the translation needs of today (and tomorrow).
In profiling the traits of Internet users versus online content, the most recently available data
reveal that the number of English-speaking users, at 800 million (28%), is followed by Chinese-speakers,
at 649 million (23%) and then drops off to 222 million (8%) for Spanish—all in a total user base of 2.8
billion—see Figure 1 (Internet World Stats, 2015; W3Techs, 2015). However, in terms of the content
available to these users, English leads at 56%, with an immediate plunge to Russian and German (both
6%), Japanese and Spanish (5%), and Chinese now at 3%. This substantial disjoint between users and
available content is largely explained by the dominance of English content and the unique development of
Internet connectivity in the uptake of Web 2.0 technologies in different countries as well as investment in
technological infrastructures such as broadband and mobile Internet.
Figure 1. Internet users and available content by
language (based on data from Internet World Stats, 2015; W3Techs, 2015).
International Journal of Communication 10(2016) The Impact of Translation Technologies 949
Growth rates add another dimension. Although growth in the number of English-speaking users
has continued steadily at a rate of about 468% from 2000 to 2013, it is overshadowed greatly by other
global languages. Chinese and Spanish grew by 1,910% and 1,123%, respectively, with other languages
showing considerable growth in the same period—for example, Arabic at 5,296% and Russian at 2,721%
(Internet World Stats, 2015; W3Techs, 2015). This trend is mirrored in the composition of the translation
industry during the same period. It has traditionally seen Europe (48.75%) and North America (35.77%)
as the largest and most developed markets in the current global market, while the emerging markets of
Asia (11.38%), Africa (0.29%) Latin America (1.80%), and Oceania (2.0%) have only recently begun to
develop and are yet to show their full potential (DePalma, Hegde, Pielmeier, & Stewart, 2013) as is also
argued here. A visual snapshot of this truly global activity is shown in Figure 2.
Figure 2. A snapshot of Internet activity during the European daylight
hours. Retrieved from http://internetcensus2012.bitbucket.org/paper.html.
Similarly, analysts from the translation industry report that only a tiny amount of digital content,
less than 0.1%, is currently being translated (DePalma et al., 2013). Indeed, the language services
market as a whole has shown consistent year-on-year growth in recent years despite the global financial
crisis, from US$23.50 billion in 2009 to US$34.78 in 2013—an annual growth rate of 5.13%. Translation
prices per word, however, have continued to decrease by up to 50% since 2008, a diminution that
analysts attribute to budgetary pressures and increased acceptance of translation technologies (DePalma
et al., 2013, pp. 8–9). With Internet users now in the billions and growth far from tapering off, translation
950 Stephen Doherty International Journal of Communication 10(2016)
technologies have been looked upon to provide solutions to this explosion of content that traditional
human translation processes simply cannot manage. These technologies have developed vis-à-vis other
information and communications technologies over recent decades and have even enabled such
developments in return by providing a means of wider and more efficient interlingual communications that
had hitherto been impossible (e.g., global simultaneous distribution of digital content such as computer
software into tens of languages), all while transforming the very nature and practice of translation.
This article adopts an interactionist approach to demonstrate how technological developments in
translation, driven by the two major technological innovations of computer-assisted translation tools and
machine translation, have fundamentally changed how we communicate today. These developments and
their concomitant positive and negative consequences are situated within the context of a fast-changing
industry and the body of accompanying interdisciplinary translation research that focuses on process,
product, and society. Thus, I critically review how translators now translate (process); what is being
translated (product); and how the role of the translator has diversified to include various professional
specializations and technical competencies as well as everyday users (society).
I contend that the ongoing technological evolution in translation has yielded unprecedented gains
in terms of increased translator productivity and consistency, greater global language coverage, and
greater support for improving international communication and distribution. However, there also exist
significant knock-on effects that these technologies have on the practice and perception of translation
itself, including the perceived and actual value of translation; the awareness and uptake of translation
technologies; and the status and visibility of the profession.
Computer-Assisted Translation Tools
Recognizing the need to translate their products in order to be successful on international
markets, software companies of the 1990s, and several other technology-related industries, sought a way
to increase productivity in translation and maintain consistency of their linguistic data across a growing
number of languages and countries (Esselink, 2000). As a result of this need and other factors such as the
increased availability and affordability of computing power and the Internet, computer-assisted translation
(CAT) tools provided the first major technological shift in the present-day translation industry with their
commercial debut in the 1990s.
The core of CAT tools is a translation memory (TM), a software program that stores a translator’s
translated text alongside its original source text, so that these pairs can later be reused in full or in part
when the translator is tasked with translating texts of a similar linguistic composition. For example, having
previously translated the following sentence from English into French:
Click on the “Next” button to go to the next step.
Cliquez sur le bouton «Suivant» pour passer à l’étape suivante.
International Journal of Communication 10(2016) The Impact of Translation Technologies 951
And then being presented with a new English sentence that contains:
Click on the “Back” button to go to the previous step.
The TM would show the translator the stored translation from the first sentence and highlight the lexical
matches, much like the find-and-replace function in contemporary word processors, but with two
languages in tandem. The matching words (as illustrated below in underlined text) in this new English
sentence propose, using the stored elements, its translation into French:
Click on the “Back” button to go to the previous step.
Cliquez sur le bouton «Suivant» pour passer à l’étape suivante.
With these suggested matches, the translator can assess their quality and contextual
appropriateness and use them in full or in part by editing (e.g., additions, deletions, substitutions). Here,
for instance, the translation for Back and previous would need to be entered manually and the translator
would substitute the verb for another to match the new context. This new English-French sentence pair
would then be saved for later reuse. Over time, TMs can contain thousands, millions, and even billions of
translations such as these, thereby increasing the likelihood of reuse once texts remain linguistically
similar (see Figure 3).
Figure 3. An example of the interface from a popular translation memory: MemoQ
from Kilgray. Retrieved from http://clone.kilgray.com/products/memoq/screenshots.
952 Stephen Doherty International Journal of Communication 10(2016)
In addition to this core feature, TMs are typically packaged with or integrated into additional software that
allows translators to manage specialized terminology in a format similar to bilingual glossaries (e.g.,
medical terms, company-specific branding); search for keywords within the TM’s database of stored
translations; and share these linguistic data with others using project management features common to
contemporary IT software.
While translation studies as a discipline and area of research has undergone many paradigm
shifts (Snell-Hornby, 2006), it has been slow to adopt such translation technologies within its mainstream,
resulting in a somewhat segregated subdiscipline (O’Hagan, 2013) that many scholars and industry
stakeholders see as a discipline in its own right (Alcina, 2008) as it possesses many unique attributes and
shares numerous fundamental commonalities with disciplines of computational linguistics and computer
science, which lie far beyond traditional translation studies.
In the translation industry, too, everyday practical and commercial needs mean that theoretical
models and approaches to translation are typically sidelined or ignored in favor of the more tangible and
immediate gains offered by translation technology solutions. The proliferation of CAT tools in the industry
and in academia quickly led to the creation of large collections of linguistic information (called corpora, the
plural form of corpus) in many language pairs and across many genres. Indeed, the English-French
sentences above could begin to form a small corpus to which we can add newly paired sentences as we
continue to translate. With the development of CAT tools, translators could, for the first time, easily create
their own collections of stored translations for later reuse in their work, for sharing with their colleagues,
and for both commercial and academic research purposes. The uptake of TMs by the majority of
translators has been consistently reported over the last two decades (Christensen & Schjoldager, 2010;
Reinke, 2013) with saturation for many translators who work in large organizations and specialized areas.
In its own parallel, machine translation (MT) had started to develop in the 1930s in the form of
mechanical multilingual dictionaries. However, it was not until the 1950s that MT enjoyed a more public
showcase as a limited, controlled but arguably automated translation process (see Hutchins, 2010). This
was widely reported on by the media in the postwar period—a time when MT was informed principally by
the disciplines of cryptography and statistics. Owing to the ever increasing availability of computing
power, linguistic data, and the growing need for automation, tangible successes of MT began to emerge in
the 1980s and 1990s, mostly using rule-based approaches, whereby sets of linguistic rules were written
manually by linguists and translators for each language pair (see Arnold, Balkan, Meijer, Humphreys, &
Sadler, 1996). Fueled by availability of the human translation data contained in the TMs that became
widespread in the late 1990s, MT research experienced a further paradigm shift from prescriptive, top-
down, rule-based approaches to descriptive, bottom-up, data-driven approaches chiefly in the form of
statistical MT—a paradigm shift that has led to the second major technological shift in contemporary
With this growing body of professional human translations in TMs becoming available in the
1990s and 2000s for an increasing number of languages, directions, genres, and text types, statistical MT
International Journal of Communication 10(2016) The Impact of Translation Technologies 953
made substantial inroads into translation technology research and development. This was quickly followed
by the more recent widespread adoption of MT by the translation industry and indeed by the general
public in the form of freely available online systems such as Google Translate
and Microsoft Bing,
both companies were already well-known IT providers with considerable resources to invest in the
research, development, and application of MT on a global scale.
Fundamentally, these statistical MT approaches use complex statistical algorithms to analyze
large amounts of data to generate a monolingual language model for each of the two given languages,
and a translation model for the translation of words and phrases from one of these languages into the
other. A decoder then uses these models to extrapolate the probability of a given word or phrase being
translated from one language into the other, where the most probable word or phrase co-occurrences are
chosen as the best translation (for a detailed description, see Hearne & Way, 2011; Kenny & Doherty,
These approaches allow for new languages to be covered without the need for handcrafted
linguistic rules once parallel data for the languages are available. The downside, however, is that these
systems are limited by their relative ignorance of linguistic information and their dependence on their own
training data. Thus, any new terms and formulations will be difficult to translate correctly, if they absent
from the systems’ data.
As MT systems are typically built directly from human translations, they truly blur the borders
between translation from a human and a machine. Today’s systems typically contain millions of human-
translated sentences from which they learn the patterns of probability, while specialized and freely
available online systems can contain even more data from thousands of translators collated over many
years. These systems are continually improving in terms of their quality and efficiency as their
infrastructures become more refined and more high-quality translation data become available. Current
and future issues lie in the quality of the TM data that the MT systems learn from, and in the trade-off
between the amount of data used and in the time taken to process it. More data increases quality to a
point, but takes longer to produce translations: from seconds and minutes to hours and even days
depending on the computational resources available.
As part of the increasingly technology-embedded workflows of translation in the 2000s, MT has
been added to the toolbox of many translators alongside TMs and other CAT tools—for some, by choice;
for others, by force and necessity. An important caveat in MT is that, much like TMs before it, it typically
works best with simplistic and repetitive linguistic features and within the same genres, domains, and
texts types. However, manual and semiautomatic methods of domain and genre classification have begun
to demonstrate improvements (e.g., Petukhova et al., 2012; Sharoff, 2007). Such texts are more easily
processed by MT systems, which of course do not possess human reasoning or contextual knowledge of a
text, its components, or its meaning(s). Indeed, several notable success stories of MT have come from
organizations that carefully implement MT as part of a larger workflow of content creation in adherence to
954 Stephen Doherty International Journal of Communication 10(2016)
strict authoring guides, linguistic preprocessing, domain-specific glossaries, and the use of translators to
assess and augment MT output to a high-quality, publishable standard (e.g., Roturier, 2009). Such
workflows represent a shift to more automation in not only the translation technologies used to process
linguistic data, but also in the overall translation project management systems required to coordinate
large numbers of translators, on- and off-site, multitudinous projects, and languages.
Changing the Process of Translation
While CAT tools and, more recently, MT have been largely accepted by practitioners and
researchers for their associated productivity and consistency gains, many translators are still adapting to
the changes that these technologies are making to the translation industry and indeed to the process of
translation itself. As most translators are freelancers or work for small-scale language service providers of
between two and five employees (DePalma et al., 2013), learning how to effectively use these
technologies poses a considerable challenge to most. Undeterred by calls for increased technological
competencies dating back to the 1990s (O’Hagan, 2013) and the recent appearance of MT as part of the
formal translation curriculum (Doherty & Kenny, 2014; Kenny & Doherty, 2014), translation technology
competencies remain an underdeveloped skill set in translator education despite extensive industry
surveys highlighting their absolute necessity and tremendous value (Gaspari, Almaghout, & Doherty,
As detailed above, the basic premise of TMs and MT is quite simple. Their integration into the
translation process, however, has resulted in considerable alterations to how translators have traditionally
worked with text. Perversely, TMs have been shown to result in a “sentence salad” (Bédard, 2000) due to
the over-recycling of sentences and parts thereof that may not suit the context and cohesion of the given
text to be translated but are reused by translators nevertheless. Further, focusing on text that only
appears at the sentence level places great difficulties on providing an accurate and fluent translation that
adheres to the cohesive and contextual norms of the target language, where, for instance, common
linguistic devices of cohesion such as anaphora and cataphora typically function at the paragraph and
document level. Indeed, translators may even opt for deliberate lexical repetition to decrease the variance
in their expression and return more TM matches—a tactic known as “peep-hole translation” that poses a
great threat to translation quality (Heyn, 1998) and consistency (Moorkens, Doherty, Kenny, & O’Brien,
Despite these dangers, Bowker (2005) points to a position of “blind faith” in TMs that has been
adopted by translators who assume that the previously used human translation in TM data is of high
quality and, as a result, are much less scrupulous in evaluating it than if they were translating from
scratch. This is compounded by the reduced remuneration for using TMs, where the rule of thumb has
been that if a certain percentage of the sentence to be translated is already provided by the TM, then the
translator has that much less work to do. Despite contrary empirical evidence to this widely held belief
(e.g., O’Brien, 2006), remuneration for translation using CAT tools has been decreasing consistently.
Moreover, in most developed markets, clients typically insist that TMs be used and may provide their own
proprietary TM data for the translator. To share these linguistic data, TMs have the function to share
access both locally and internationally via local networks, servers, and cloud-based applications. There
International Journal of Communication 10(2016) The Impact of Translation Technologies 955
also exist numerous collections of TM data for commercial and noncommercial use (e.g., the European
and the Translation Automation User Society
). While shared TMs have great potential
for leveraging existing translation data, thus increasing productivity, issues of ethics (e.g., Kenny, 2011),
preservation of consistent quality (Moorkens et al., 2014), and secure storage all become inevitable points
of concern that I wish to further emphasize.
With the availability of large bilingual corpora as provided by TMs and used in MT applications,
other aspects of translation have come under study in addressing the calls for corpus-based and empirical
research (Bowker, 2002; Holmes, 2000) of translation that emerged in the 1990s “to uncover the nature
of the translated text as a mediated communicative event” (Baker, 1993, p. 243). Corpus-based
approaches have since been used as an evaluative framework for translation quality assessment (Bowker,
2001) and translator training (Bowker, 2003), and can remove subjectivity and ambiguity in that they
provide authentic texts that can be used by translators (and evaluators) to justify and verify choices in the
translation process and in assessing the severity and impact of translation errors.
Access to this bilingual data also allows for the study of the universal features of translation as
well as language- and direction-specific features of the translation process (e.g., Bowker, 2003; Olohan &
Baker, 2000). Such research has uncovered insightful and useful patterns, such as lexical simplification in
translation (e.g., Laviosa, 2002), explicitation (e.g., Klaudy, 1998), increased use of standard forms of
language and the inescapable influence of the linguistic structure of the source text on translation choices
(e.g., Toury, 1995). Similarly, these data also paved the way for comparative multidimensional evaluation
of translation quality, including readability and comprehension (e.g., Doherty, 2012) and diagnostic
evaluation (e.g., Gaspari et al., 2014) as well as measures of usability (e.g., Doherty & O’Brien, 2014) and
cognitive effort (e.g., Doherty, O’Brien, & Carl, 2010).
Following a similar trajectory toward empiricism, translation process studies have emerged to
focus on the translator and the process of translation rather than on the end product—see an example in
Figure 4. These studies have been gradually mapping the cognitive and psycholinguistic elements of the
translation process to uncover more about how translators work, how they use TMs and MT, and how
teaching can be refined. This stream of research has incorporated qualitative, quantitative, and mixed-
method designs that marry the subjective experience of this complex cognitive processing with more
objective observations, all while trying to preserve the ecological validity of a real translation process.
Although further development is needed in terms of methodological refinements drawn from other more
mature empirical disciplines (see Doherty, in press), this body of research has nevertheless demonstrated
unique advantages over psycholinguistics and cognitive sciences, which typically focus on experiments
with lower ecological validity and smaller units of text that are of limited use in the real-world contexts of
956 Stephen Doherty International Journal of Communication 10(2016)
Figure 4. An example of a typical reading pattern recorded by an
eye tracker. Retrieved from http://en.wikipedia.org/wiki/Eye_tracking.
Translation process studies have incorporated keystroke logging (e.g., Jakobsen & Schou, 1999;
Van Maes & Leijten, 2006), eye tracking (e.g., Doherty et al., 2010; Dragsted, 2010; Jensen, 2008), brain
imaging (e.g., Grabner, Brunner, Leeb, Neupera, & Pfurtscheller, 2007) and continue to present
researchers with opportunities to further explore the cognitive aspects of translation (e.g., Göpferich,
Jakobsen, & Mees, 2008; Shreve & Angelone, 2010). From this body of relatively recent scholarship,
tangible results can already be found in the form of insights into translation subprocesses (e.g., Göpferich
et al., 2008; Mossop, 2001), differences between professionals and amateurs (e.g., Dragsted, 2010), and
translators’ interactions with CAT tools (e.g., O’Brien, 2008). These examples are but a few of those that
have yielded considerable contributions to the evidence-based teaching and practice of translation.
The Changing Product of Translation
Translation has traditionally come in the form of literary, religious, political, and technical texts.
These well-defined genres have expanded to include commercial content (e.g., marketing, product
descriptions, patents, support documentation, and business communications) as well as a wider range of
technical genres such as scientific research, medical and pharmaceutical documentation, and patient
information. Although these areas have traditionally enjoyed continuous growth, since the 1990s, an
unprecedented need has arisen to translate digital content such as websites, computer software, technical
documentation, video games, and subtitles. With such a wide variety of content, there is also a particular
focus on the requirements of specific audiences in geographic and linguistic locales, often referred to as
Often seen as an extension of traditional translation processes, localization can be
characterized in terms of the three interconnected features of the product to be
localized: “linguistically as translating a product to suit the target users, technically as
International Journal of Communication 10(2016) The Impact of Translation Technologies 957
adjusting technology specifications to suit the local market, and culturally as following
the norms and conventions of the target community” (Chan, 2013, p. 347).
The text types and formats of localized content differ considerably from traditional texts in that
the former contains domain-specific neologistic terminology and language conventions, computer code,
and unique file formats and structures that are also often specific to languages and regions. Thus,
translators working with such content require specialized training to effectively deal with these
extralinguistic features, identify translatable elements (Pym, 2010), and navigate complex software
functionality and usability requirements—for example, spacing constraints on websites and text-embedded
Furthermore, unlike traditional texts, digital content tends to be more perishable in nature owing
to the need to update information on- and off-line in a regular and continuous fashion. Cronin (2013)
notes a move from “content being rolled out in a static, sequential manner” to translated content being
“integrated into a dynamic system of ubiquitous delivery” (p. 498). These “living texts” (O’Hagan, 2007)
mix linguistic and sociocultural information with technical content that needs to be carefully localized to
specific market regions with unique requirements, functionality, and expectations, especially for software
and video games (Chandler, 2005).
In line with the growth in the amount and diversity of content to be translated, globalization and
expanding international markets have resulted in more languages requiring translation. In the early
2000s, the most common language combinations were from English into French, Italian, German, Spanish,
Brazilian Portuguese, and Japanese (Chan, 2013). However, since then, sustained growth on a global
scale, especially in Asia, has seen translation into tens of languages and hundreds of regions. A case in
point is Apple, which currently localizes into about 40 languages across 150 countries with text input
methods for 50 languages and their variants
—a model that is being viewed as the leading approach to
technology-enabled simultaneous global distribution.
Recent industry data show the localization industry alone growing at an average rate of 30% each
year, resulting in the proliferation of localization-specific courses at universities and professional bodies
and within large companies and organizations (Chan, 2013). Much of this burgeoning digital content is
audiovisual translation, principally concerning subtitling, accessibility (e.g., Gambier, 2013), and reception
(e.g., Sasamoto & Doherty, 2015). Audiovisual translation, too, has seen the sometimes seamless,
sometimes haphazard integration of TM and/or MT into existing proprietary and open-source audiovisual
translation software (see Figure 5). Applications range from the standard usage of TMs for subtitling to
using full MT (e.g., Armstrong et al., 2007; Müller & Volk, 2013). Significant quality issues include a
substantial and lingering limitation to widespread application due to the vast variation in genres and user
needs, especially when some users of machine-translated subtitles may be more vulnerable to errors—for
example, viewers with hearing impairments.
958 Stephen Doherty International Journal of Communication 10(2016)
Figure 5. An example of open-source audiovisual translation software widely
used in online communities: Aegisub. Retrieved from http://www.aegisub.org.
Translation Technologies and Quality
Despite the widespread and diverse adoption of MT in research and practice, most machine-
translated content still requires some form of human intervention to edit the MT output to the desired
level of quality and/or to verify its quality before publication, dissemination, product release, legal
compliance, and so on. This question of quality, to which I now turn, has been extensively researched in
the academic literature on translation and, more recently, within the translation industry given the
application of the question of quality to translations produced by machines.
Throughout the long-standing debate on what is a good (or bad) translation, I propose that a
dichotomy between accuracy and fluency is apparent across translation theory, translation technology,
and in the translation industry in one guise or another, where accuracy typically denotes the extent to
which the meaning of the source text is rendered in its translation, and fluency denotes the naturalness of
the translated text in terms of the norms of that language. The primary goal of assessing translation
quality is ensuring that a specified level of quality is reached, maintained, and delivered as part of the
translation product. The debate on translation quality (e.g., House, 1997; Nord, 1991; Reiss, 2000) was
far from being resolved prior to the advent of TM and MT, and, unsurprisingly, the widespread adoption of
such translation technologies has only added fuel to a renewed debate on translation quality assessment,
pricing for MT in the industry, and risks to everyday users.
In terms of quality assessment of MT, the industry departs from traditional academic debate due
in part to a vast divergence between research and practice on this topic and also to the need for resource-
International Journal of Communication 10(2016) The Impact of Translation Technologies 959
efficient means of quality assessment. Although much human evaluation of MT is carried out under the
adequacy and fluency paradigm (e.g., Koehn, 2010), it remains resource-intensive and has led to the
development of automatic evaluation metrics—algorithms that assess MT quality based on its comparison
to a human translation by counting the number of matching words or the number of edits required to
enable the MT output to match that of a human translator. Although such means of assessing quality is far
from the sophistication of human judgment, it provides a quick and dirty solution that is especially
valuable in research and development.
Automatic evaluation metrics have since become more commonplace in industry applications
(e.g., in cloud-based MT systems such as KantanMT
), yet awareness of what they can and cannot
measure remains a critical issue that cannot be understated. The absence and unintentional misuse of
quality assessment in MT often occurs, and users consequently make uninformed decisions leading to
incorrect judgments as to how suitable the machine-translated content is for dissemination. A simple Web
search yields an endless list of examples of “bad” MT by everyday users, who are largely unable to assess
its quality due to the language barrier and absence of reliable indicators, and may therefore have to
blindly trust in its quality. While examples in restaurants and on billboards may be humorous (see Figure
6), MT has also gained a foothold in commercial and public-service translation, where it is increasingly
being used in schools, hospitals, and public services in some countries in a desperate attempt to make
content available in more languages, where, once again, human translation remains costly and slow (e.g.,
Randhawa, Ferreyra, Ahmed, Ezzat, & Pottie, 2013; Turner, Bergman, Brownstein, Cole, & Kirchoff,
Figure 6. An everyday example of bad machine translation: The source text reads
“restaurant” in Chinese. Retrieved from http://www.flickr.com/photos/chijs/2647542165.
960 Stephen Doherty International Journal of Communication 10(2016)
Although substantial improvements in the quality of commercial MT systems are clearly evident,
even the best contemporary MT systems frequently produce errors that require some degree of human
intervention. This method of fixing MT output, known as post-editing, has become significant in translation
research and throughout the industry on a global scale (DePalma, 2013). Much like the push to use TMs
experienced in the 1990s and 2000s, translation buyers, hesitant to fully rely on MT, are implementing
post-editing incrementally in the face of budget constraints, increased time pressure for project
turnaround, and a trend toward the increased casualization of the translation profession.
Rates for post-editing tend to be even lower than translation with CAT tools, often by as much as
60% depending on the market and location (DePalma, 2013), yet the range of its applications is quite
diverse. It is often the case that different levels of post-editing (light and heavy) are required to reach a
designated level of quality: “gisting” (e.g., for comprehension of the main points of a text); medium
quality for internal communications, knowledge, and information sharing (e.g., corporate communications
across multiple sites, sharing drafts); and high-quality publishable content for direct public consumption.
In addition to the various levels of post-editing, translators must master a new skill set of language-
specific linguistic and technical techniques that may not be readily available to traverse the learning curve
associated with post-editing MT output.
Society: Professional and Everyday Translators
Evident from the previous examples of the changes translation technologies have brought to what
is being translated and how it is being translated, technologies also have changed the who of translation in
that such technologies have opened up access and interest to translation, especially with regard to user-
generated content, social media, and audiovisual translation. Indeed, one of the most substantial
technological developments of the past decade has been the shift from desktop computing to distributed
and ubiquitous computing (Dennis & Urry, 2007), a trend that has enabled the flourishing of Web 2.0
technologies, also known as the “user-generated web” (van Dijck, 2009).
The rise of this user-participatory culture (Jenkins, 2006) and the complex relationship between
cognitive surplus (Shirky, 2010) and online social capital (Shah, Kwak & Holbert, 2001), added to the
availability of translation technologies within the open-source community, has led to everyday users with
varying degrees of foreign language proficiency functioning as amateur and volunteer translators:
translating online content, working on large online projects, and even evaluating the quality of translations
for their area of interest (e.g., social media, video games, animation). This phenomenon has had
considerable impact in research and industry circles alike, leading to the widespread recognition within the
translation community of specialized terms such as “user-generated translation” (O’Hagan, 2009), online
“community translation” (O’Hagan, 2011) and “open translation” (DePalma & Kelly, 2008). Undoubtedly,
such practices pose an additional threat to professional translators who have expressed widespread
concerns about the quality (e.g., O’Hagan, 2013), and ethics of this digital ontogenesis (e.g., Drugan,
In addition to this willing and able online workforce of amateur translators, Web 2.0 technologies
have opened the door to more users to access the Internet and actively create and share their own
International Journal of Communication 10(2016) The Impact of Translation Technologies 961
content, which, in turn, is likely to need translation to reach a wider global audience—for example,
blogging, social media, and technical support fora (e.g., Mitchell, O’Brien, & Roturier, 2014). It is for such
user-generated content that users with proficiency in foreign languages become volunteer and amateur
translators of their own and other users’ content (see O’Hagan, 2009). Some incarnations of this so-called
crowd-sourced translation have come in the form of nonprofit ventures such as the Wikipedia movement,
Translators without Borders,
and the Rosetta Foundation,
while others are entirely commercial
operations where crowd sourcing is used as part of the marketing and/or distribution campaign for the
brand, product, or service. Facebook, for example, adopted a crowd-sourcing model to allow its users to
translate content from English, in which they had various degrees of proficiency, into their own native
language communities (Kelly, Ray, & DePalma, 2011).
Outputs from crowd-sourced translation come in many of the same forms as traditional forms of
translation, from traditional text documents, to websites, technical support documentation, instruction
manuals, and audiovisual translation. Fan subtitling of popular TV programs and movies, known as
“fansubs” (O’Hagan, 2009), has become a mainstream alternative to existing subtitles that fans claim can
be lackluster due to the translation being carried out by professional translators who are not fans
themselves. Actual and perceived censorship in official translations and subtitles are also bypassed by the
sheer popularity of fan-created alternatives that are freely available on the Internet and created by
amateur, volunteer translators using open-source translation technologies and techniques freely and often
loosely adopted from translation studies literature—for example, presentation and timing of subtitles.
Freely available (but not actually free) online technologies such as Google Translator Toolkit
provide TM and MT functionalities in addition to integrated instant messaging, shared calendars, and
cloud-based storage solutions, offering a comprehensive, “professional” suite of tools that can be used by
amateur translators for a plethora of crowd-sourcing endeavors.
Finally, moving beyond the use of translation technologies by professional and amateur
translators, everyday users also have found MT systems becoming household names—for example, Google
Translate and Microsoft Bing, with Google boasting a growing user base of more than 200 million each day
(Shankland, 2013). The usage scenarios of everyday users range from personal tasks such as searching
for information on travel, shopping, technical support, and language learning to commercial product and
market research, communicating with customers and suppliers, and opening up new markets. Various
professions, including teachers and health care professionals, use freely available online MT so that they
can communicate with their clients who do not speak their language—a trend especially pronounced with
large-scale migration and displacement. Once again, issues of quality, legality, responsibility, and
remuneration all come into play.
Although the need for translation in such cases is clear, the use of freely available online MT
systems is a cause for grave concern, especially in sensitive intercultural scenarios where professional
962 Stephen Doherty International Journal of Communication 10(2016)
translation (and interpreting) services are a necessity. However, given budgetary constraints and the
reactive nature of providing for new and emerging languages to new geographic locations, it can take time
for the provision of professional services to come into place, if they are provided at all. In such cases,
many everyday users can, and do, choose MT for professional and personal use and remain unaware of
the strong potential for poor quality resulting in misunderstanding, miscommunication, and liability.
Conclusion: The Obfuscation of Human and Machine Translation
In exploring the impact of translation technologies on international communication from an
interactionist perspective, the effects on the translation process, its products, and its place in society are
all remarkably palpable. Technological developments in the early 1990s led to the widespread uptake of
CAT tools, chiefly TMs, which have created an increase in productivity and consistency in translation but a
decrease in remuneration, control, and risks to overall quality. TMs then paved the way for state-of-the-
art MT systems that use human translations to emulate the results of the translation process and deliver
output in speeds and volumes that will never be achieved by human translators alone. MT, however, is not
without its own risks to quality, misrepresentation, and misuse, and it presents another force that
translators must contend with as the fixing of machine-translated output becomes the bread and butter of
many professional translators.
Moreover, as the sophistication of MT improves, its reliance on human translation data is
becoming more difficult to identify as the lines between human and machine are continually blurred and
professional translators become more reliant and embedded into the translation process that they had
hitherto controlled. This is compounded by the explosion of amateur, volunteer translators making use of
such tools to diffuse the rapidly growing amount of digital content created on a daily basis in many
languages, in many countries, and for many purposes. In the wake of TMs and MT software, the need for
technological competencies for professional translators to remain on top, if not ahead, of change has
never been more evident than it is now. With informed and effective use of TMs and MT, many of the
known issues and shortcomings of these technologies can be overcome, especially in terms of translation
quality, to somewhat mitigate the downward trend in pricing for translation services in line with tighter
budgets and deadlines. Further empirical evidence of the effects that these tools have on productivity,
consistency, and quality will add value to negotiations of fair and appropriate pricing and evidence-based
best practices within the industry and academe—an agenda that is in need of much more collaborative
However, these new technologies have, in turn, allowed for the creation of novel content types
and newly created professional translation-related roles in the course of their own development—for
example, localization, post-editing, project management, and quality assessment—and they allow
(machine) translation to reach languages that were hitherto neglected due to perceived insufficient
commercial viability and demand. This is a provision that many users are content with, even if the MT
output is not of the best quality, because it is simply better than nothing at all.
By extension, then, the technological developments in the form of TMs and MT have had, and
continue to have, considerable widespread repercussions for translators and nontranslators alike across
International Journal of Communication 10(2016) The Impact of Translation Technologies 963
everyday personal and professional scenarios, where the visibility of the human translator has been
opaqued by a growing selection of relatively easy-to-use and online MT systems that do not readily show
users where their translations have come from and how good the quality is. To the everyday user, MT has
become a household name under the guise of Google Translate and, to a lesser extent, Microsoft Bing.
Such users are becoming increasingly accustomed to being able to access “free” translation services at the
touch of a button as the presence of MT becomes much more commonplace and translation ergo becomes
less valued and visible.
In looking ahead, what remains unclear is the particular roles that translators and everyday users
of translation will play in an increasingly technology-dependent globalized society. As translation
technologies intersect and sometimes subsume the translation process entirely, an important factor in
moving toward the effective use of these technologies and in preparing for future changes is a critical and
informed approach in understanding what such tools can and cannot do and how users should use them to
achieve the desired result. It is here that I insist upon the emergent need for the fundamental awareness
of and accessible education for translation technologies, their strengths and weaknesses, and their impact
on international and intercultural communications for all stakeholders, including translators, buyers and
sellers of translation services, and, most of all, the everyday user who is the most unaware and
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