ArticlePDF Available

The impact of translation technologies on the process and product of translation


Abstract and Figures

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.
Content may be subject to copyright.
International Journal of Communication 10(2016), 947969 19328036/20160005
Copyright © 2016 (Stephen Doherty). Licensed under the Creative Commons Attribution Non-commercial
No Derivatives (by-nc-nd). Available at
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,
globalization, localization
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
search engines.
Stephen Doherty:
Date submitted: 20141118
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 Spanishall in a total user base of 2.8
billionsee 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 periodfor 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
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 2013an 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. 89). 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
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.
Machine Translation
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 perioda 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 MTa 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 toolsfor 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
Commission’s TM,
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 productsee 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
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 requirementsfor 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 errorsfor
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
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 metricsalgorithms 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
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 audiencefor 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 literaturefor 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 namesfor 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 languagea 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 academean 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 developmentfor
example, localization, post-editing, project management, and quality assessmentand 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
Alcina, A. (2008). Translation technologies: Scope, tools and resources. Target, 20(1), 79102.
Armstrong, S., Way, A., Caffrey, C., Flanagan, M., Kenny, D., & O’Hagan, M. (2007). Leading by example:
Automatic translation of subtitles via EBMT. Perspectives: Studies in Translatology, 14(3), 163
Arnold, D., Balkan, L., Meijer, S., Humphreys, R., & Sadler, L. (1996). Machine translation: An
introductory guide. Oxford, UK: Blackwell.
Baker, M. (1993). Corpus linguistics and translation studies: Implications and applications. In M. Baker, G.
Francis, & E. Tognini-Bonelli (Eds.), Text and technology: In honour of John Sinclair (pp. 233
250). Amsterdam, The Netherlands: John Benjamins.
Bédard, C. (2000). Mémoire de traduction cherche traducteur de phrases [Translation memory seeks
sentence translator]. Traduire, 186, 4149.
Bowker, L. (2001). Towards a methodology for a corpus-based approach to translation evaluation. Meta,
46(2), 345364.
Bowker, L. (2002). Computer-aided translation technology: A practical introduction. Ottawa, Canada:
University of Ottawa Press.
964 Stephen Doherty International Journal of Communication 10(2016)
Bowker, L. (2003). Corpus-based applications for translator training: Exploring the possibilities. In S.
Granger, J. Lerot, & S. Petch-Tyson (Eds.), Corpus-based approaches to contrastive linguistics
and translation studies (pp. 169184). Amsterdam, The Netherlands: Rodopi.
Bowker, L. (2005). Productivity vs quality? A pilot study on the impact of translation memory systems.
Localisation Focus, 4(1), 1320.
Chan, S. (2013). Approaching localization. In C. Millán & F. Bartrina (Eds.), The Routledge handbook of
translation studies (pp. 347362). London, UK: Routledge.
Chandler, H. (2005). The game localisation handbook. Hingham, MA: Charles River Media.
Christensen, T., & Schjoldager, A. (2010). Translation-memory (TM) research: What do we know and how
do we know it. HermesJournal of Language and Communication Studies, 44, 89101.
Cronin, M. (2013). Translation and globalization. In C. Millán & F. Bartrina (Eds.), The Routledge handbook
of translation studies (pp. 491502). London, UK: Routledge.
Dennis, K., & Urry, J. (2007). The digital nexus of post-automobility. Lancaster, UK: Lancaster University.
DePalma, D. (2013). How to add post-edited MT to your service offerings. Cambridge, MA: Common
Sense Advisory.
DePalma, D., Hegde, V., Pielmeier, H., & Stewart, R. (2013). The languages services market. Cambridge,
MA: Common Sense Advisory.
DePalma, D., & Kelly, N. (2008). Translation of, for, and by the people. Lowell, MA: Common Sense
Doherty, S. (2012). Investigating the effects of controlled language on the reading and comprehension of
machine translated texts (Unpublished doctoral dissertation). Dublin City University, Dublin,
Doherty, S. (in press). Improving standards in translation quality assessment with psychometric
principles. In D. Kenny (Ed.), Human issues in translation technology: The IATIS yearbook.
London, UK: Routledge.
Doherty, S., & Kenny, D. (2014). The design and evaluation of a statistical machine translation syllabus
for translation students. The Interpreter and Translator Trainer, 8(2), 295315.
Doherty, S., & O’Brien, S. (2014). Assessing the usability of raw machine translated output: A user-
centered study using eye tracking. International Journal of Human-Computer Interaction, 30(1),
International Journal of Communication 10(2016) The Impact of Translation Technologies 965
Doherty, S., O’Brien, S., & Carl, M. (2010). Eye tracking as an MT evaluation technique. Machine
Translation, 24(1), 113.
Dragsted, B. (2010). Coordination of reading and writing processes in translation. In G. Shreve & E.
Angelone (Eds.), Translation and cognition (pp. 4161). Amsterdam, The Netherlands John
Drugan, J. (2011). Translation ethics wikified: How far do professional codes of ethics and practice apply
to non-professionally produced translation? Linguistica Antverpiensia, 10, 111126.
Esselink, B. (2000). A practical guide to localization. Amsterdam, The Netherlands: John Benjamins.
Gambier, Y. (2013). The position of audiovisual translation studies. In C. Millán & F. Bartrina (Eds.), The
Routledge handbook of translation studies (pp. 4559). London, UK: Routledge.
Gaspari, F., Almaghout, H., & Doherty, S. (2015). A survey of machine translation competences: Insights
for translation technology educators and practitioners. Perspectives: Studies in Translatology,
23(3), 333358.
Gaspari, F., Toral, A., Lommel, A., Doherty, S., van Genabith, J., & Way, A. (2014, May 26). Relating
translation quality barriers to source-text properties. Paper presented at the International
Conference on Language Resources and Evaluation, Reykjavik, Iceland.
Göpferich, S., Jakobsen, A., & Mees, I. (Eds.). (2008). Looking at eyes: Eye-tracking studies of reading
and translation processing. Copenhagen, Denmark: Samfundslitteratur.
Grabner, R., Brunner, C., Leeb, R., Neuper, C., & Pfurtscheller, G. (2007). Event-related EEG theta and
alpha band oscillatory responses during language translation. Brain Research Bulletin, 72(1), 57
Hearne, M., & Way, A. (2011). Statistical machine translation: A guide for linguists and translators.
Language and Linguistics Compass, 5, 205226.
Heyn, M. (1998). Translation memory: Insights and prospects. In L. Bowker, M. Cronin, D. Kenny, & J.
Pearson (Eds.), Unity in diversity (pp. 123136). Manchester, UK: St. Jerome.
Holmes, J. (2000). The name and the nature of translation studies. In L. Venuti (Ed.), The translation
studies reader (pp. 172185). London, UK: Routledge.
House, J. (1997). Translation quality assessment. Tübingen, Germany: Gunter Narr Verlag.
Hutchins, W. (2010). Machine translation: A concise history. Journal of Translation Studies, 13(12), 29
966 Stephen Doherty International Journal of Communication 10(2016)
Internet World Stats. (2015). Usage and population statistics: World users by language. Retrieved from:
Jakobsen, A., & Schou, L. (1999). Translog documentation. In G. Hansen (Ed.), Copenhagen studies in
language, 24 (pp. 151186). Copenhagen, Denmark: Samfundslitteratur.
Jenkins, H. (2006). Convergence culture: Where old and new media collide. New York, NY: New York
University Press.
Jensen, C. (2008). Assessing eye-tracking accuracy in translation studies. In S. Göpferich, A. Jakobsen, &
I. Mees (Eds.), Copenhagen studies in language, 36 (pp. 157174). Copenhagen, Denmark:
Kelly, N., Ray, R., & DePalma, D. (2011). From crawling to sprinting: Community translation goes
mainstream. Linguistica Antverpiensia, 10, 7594.
Kenny, D. (1996). It looks for all the world as if Günter Grass writes in English. Translation Ireland, 10(3),
Kenny, D. (2011, June 4). The ethics of machine translation. Paper presented at the New Zealand Society
of Translators and Interpreters annual conference, Auckland, New Zealand.
Kenny, D., & Doherty, S. (2014). Statistical machine translation in the translation curriculum: Overcoming
obstacles and empowering translators. The Interpreter and Translator Trainer, 8(2), 276294.
Klaudy, K. (1998). Explicitation. In M. Baker (Ed.), Encyclopedia of translation studies (pp. 8085).
London, UK: Routledge.
Koehn, P. (2010). Statistical machine translation. Cambridge, UK: Cambridge University Press.
Laviosa, S. (2002). Corpus-based translation studies: Theory, findings, applications. Amsterdam, The
Netherlands: Rodopi.
Mitchell, L., O’Brien, S., & Roturier, J. (2014). Quality evaluation in community post-editing. Machine
Translation, 28(34), 237262.
Moorkens, J., Doherty, S., Kenny, D., & O’Brien, S. (2014). A virtuous circle: Laundering translation
memory data using statistical machine translation. Perspectives: Studies in Translatology, 22(3),
Mossop, B. (2001). Revising and editing for translators. Manchester, UK: St. Jerome.
International Journal of Communication 10(2016) The Impact of Translation Technologies 967
Müller, M., & Volk, M. (2013). Statistical machine translation of subtitles: From OpenSubtitles to TED. In I.
Gurevych, C. Biemann, & T. Zesch (Eds.), Language processing and knowledge in the Web (pp.
132138). Berlin, Germany: Springer.
Nord, C. (1991). Scopos, loyalty and translational conventions. Target, 3(1), 91109.
O’Brien, S. (2006). Eye-tracking and translation memory matching. Perspectives: Studies in Translatology,
14(3), 185205.
O’Brien, S. (2008). Processing fuzzy matches in translation memory toolsan eye-tracking analysis. In S.
Göpferich, A. Jakobsen, & I. Mees (Eds.), Copenhagen studies in language, 36 (pp. 79102).
Copenhagen, Denmark: Samfundslitteratur.
O’Hagan, M. (2007). Video games as a new domain for translation research: From translating text to
translating experience. Revista Tradumàtica, 5, 17.
O’Hagan, M. (2009). Evolution of user-generated translation: Fansubs, translation hacking and
crowdsourcing. Journal of Internationalization and Localization, 1(1), 94121.
O’Hagan, M. (2011). Community translation: Translation as a social activity and its possible consequences
in the advent of Web 2.0 and beyond. Linguistica Antverpiensia, 10, 1123.
O’Hagan, M. (2013). The impact of new technologies on translation studies: A technological turn? In C.
Millán & F. Bartrina (Eds.), The Routledge handbook of translation studies (pp. 503518).
London, UK: Routledge.
Olohan, M., & Baker, M. (2000). Reporting that in translated English: Evidence for subconscious processes
of explicitation? Across Languages and Cultures, 1(2), 141158.
O’Reilly, T. (2005, September 30). What is Web 2.0: Design patterns and business models for the next
generation of software. Retrieved from
Petukhova, V., Agerri, R., Fishel, M., Georgakopoulou, Y., Penkale, S., del Pozo, A., Maučec, M., Volk, M.,
& Way, A. (2012, May 23). SUMAT: Data collection and parallel corpus compilation for machine
translation of subtitles. Paper presented at the International Conference on Language Resources
and Evaluation, Istanbul, Turkey.
Pym, A. (2010). Exploring translation theories. London, UK: Routledge.
Randhawa, G., Ferreyra, M., Ahmed, R., Ezzat, O., & Pottie, K. (2013). Using machine translation in
clinical practice. Canadian Family Physician, 59(4), 382383.
968 Stephen Doherty International Journal of Communication 10(2016)
Reinke, U. (2013). State of the art in translation memory technology. Translation: Computation, Corpora,
Cognition, 3(1), 2748.
Reiss, K. (2000). Translation criticism: The potentials and limitations. Manchester, UK: St. Jerome.
Roturier, J. (2009). Deploying novel MT technology to raise the bar for quality: A review of key
advantages and challenges. In L. Gerber, P. Isabelle, R. Kuhn, N. Bemish, M. Dillinger, &
M. Goulet (Eds.), Proceedings of the twelfth Machine Translation Summit (pp. 18). Ottawa,
Canada: Association for Machine Translation in the Americas.
Sasamoto, R., & Doherty, S. (2015). Towards the optimal use of impact captions on TV programmes. In
M. O’Hagan & Q. Zhang (Eds.), Conflict and communication: A changing Asia in a globalising
world (pp. 210247). Bremen, Germany: EHV Academicpress.
Shah, D., Kwak, N., & Holbert, R. (2001). Connecting and disconnecting with civic life: Patterns of
Internet use and the production of social capital. Political Communication, 18(2), 141162.
Shankland, S. (2013, May 18). Google Translate now serves 200 million people daily. Retrieved from
Sharoff, S. (2007). Classifying Web corpora into domain and genre using automatic feature identification.
Cahier du Cental, 5, 110.
Shirky, C. (2010). Cognitive surplus: Creativity and generosity in a connected age. London, UK: Allen
Shreve, G., & Angelone, E. (Eds.). (2010). Translation and cognition. Amsterdam, The Netherlands: John
Snell-Hornby, M. (2006). The turns of translation studies. Amsterdam, The Netherlands: John Benjamins.
Toury, G. (1995). Descriptive translation studies and beyond. Amsterdam, The Netherlands: John
Turner, A., Bergman, M., Brownstein, M., Cole, K., & Kirchhoff, K. (2014). A comparison of human and
machine translation of health promotion materials for public health practice: Time, costs, and
quality. Journal of Public Health Management and Practice, 20(5), 523529.
van Dijck, J. (2009). Users like you? Theorizing agency in user-generated content. Media, Culture and
Society, 31, 4158.
Van Maes, L., & Leijten, M. (2006). Logging writing processes with Inputlog. In L. Van Maes, M. Leijten, &
D. Neuwirth (Eds.), Writing and digital media (pp. 158165). Oxford, UK: Elsevier.
International Journal of Communication 10(2016) The Impact of Translation Technologies 969
W3Techs. (2015). Usage of content languages for websites. Retrieved from
... In our examined studies, human evaluation was performed mainly through the task of post-editing. There have been hardly any studies (Hu et al. 2019) asking viewers to directly assess the quality of automatically generated subtitles, possibly because MT engines have not been considered usable for broadcast purposes without human intervention (Doherty 2016 (2020)). The studies do not report whether subtitlers had access to the video while performing the task, which is considered good practice in industry. ...
Full-text available
Recent developments in neural machine translation, and especially speech translation, are gradually but firmly entering the field of audiovisual translation (AVT). Automation in subtitling is extending from a machine translation (MT) component to fully automatic subtitling, which comprises MT, auto-spotting and automatic segmentation. The rise of this new paradigm renders MT-oriented experimental designs inadequate for the evaluation and investigation of automatic subtitling, since they fail to encompass the multimodal nature and technical requirements of subtitling. This paper highlights the methodological gaps to be addressed by multidisciplinary efforts in order to overcome these inadequacies and obtain metrics and methods that lead to rigorous experimental research in automatic subtitling. It presents a review of previous experimental designs in MT for subtitling, identifies their limitations for conducting research under the new paradigm and proposes a set of recommendations towards achieving replicability and reproducibility in experimental research at the crossroads between AVT and MT.
... The findings of this study are in line with the studies of Baker (1995), Zanettin (1998;, Krüger (2012), and Alhassan et al. (2021) which emphasized the importance of corpus-driven pedagogy in the translation and language classrooms. Corpora tools like other computer-assisted translation tools have increased productivity, consistency and quality in translation (Doherty, 2016). This finding, however, is in conflict with a recent study that was conducted in the Russian-French context (Usmanova et al., 2021), which concluded that novice translators' overreliance on some CAT tools may negatively affect the quality of their translation. ...
Full-text available
This manuscript investigates to what extent the use of corpora could help translation trainees while translating from Arabic into English and vice versa. Forty Yemeni trainees, who were enrolled in an advanced course in Arabic-English translation during the academic year 2020, participated in the study. They participated in translation projects from which the data for this study was collected, using thinking aloud protocols and computational observation. The translation process was investigated using the translation process software Transalog, an eye-tracking software and the screen recording software Screen-O-Matic. This kind of computational observation enabled a researcher to discover the extent to which the participants were able to employ corpora in their translation projects. At the end of the study, the participants were given a questionnaire with the aim of finding out their perceptions toward the use of corpora in their translation projects, and toward the project-based training approach adopted in the study. The findings of the translation process indicated that the trainees employed various kinds of corpora in their translation projects. Results from the questionnaire showed that the trainees have very positive attitudes toward the progress in their instrumental translation sub-competence, the utilization of corpora tools, and the project-based training approach adopted in this study.
... In contrast to the limitations of face-to-face interpreting, remote interpreting, like other language technologies (see Doherty, 2016), appears to offer a more accessible and efficient means of interlingual communication that could address many of the aforementioned physical limitations, particularly around the access to a greater number of language combinations and a larger pool of interpreters with specializations (e.g., legal). However, there are numerous inherent risks associated with remote interpreting which may have a detrimental impact on the quality of interpreting and thus impair and impede communication between parties. ...
Full-text available
Remote interpreting via video-link is increasingly being employed in investigative interviews chiefly due to its apparent increased accessibility and efficiency. However, risks of miscommunication have been shown to be magnified in remote interpreting and empirical research specifically on video-link remote interpreting is in its infancy which greatly limits the evidence base available to inform and direct evidence-based policy and best practice, particularly in the identification of the optimal mode(s) of interpreting to be used, namely consecutive and simultaneous. Consecutive interpreting refers to a process in which the interpreter transfers short segments of speech from one language into the other as each person speaks in managed turn-taking, while simultaneous interpreting refers to the transfer of natural speech from one language into another in a concurrent manner without the need for speakers to segment their speech. This study provides novel empirical evidence by using eye tracking to compare the overt visual attention of interpreters working in a remote setting in which an English-speaking Interviewer interacts with a non-English-speaking Suspect in person, for whom interpretation is provided via video-link in real time. Using a within-subject design, we analyze eye-movement data from 28 professionally accredited interpreters who interpreted via video-link an investigative interview in which consecutive and simultaneous interpreting modes were counterbalanced. Taking interpreting performance into account, our results showed that, the consecutive mode yielded significantly less gaze time and therefore significantly less on-screen overt visual attention due to off-screen notetaking, an essential component of the consecutive interpreting mode. Relative to gaze time, the consecutive mode also resulted in significantly more and longer fixations and shifts of attention. Participants also allocated significantly more overt visual attention to the Interviewer than the Suspect, particularly in the consecutive mode. Furthermore, we found informative significant correlations between eye tracking measures and interpreting performance: accuracy, verbal rapport, and management. Finally, we found no significant differences between the three language pairs tested. We conclude with a discussion of limitations and the contributions of the study and an outline for future work on this topic of growing importance.
... The role they play in our life is increasingly important, and, over the past few years, the number of studies on multimodal communication has been growing steadily despite AVT still being in its early ongoing stages of development. This owes much to the mass production of audiovisual and digital products, which is far beyond the production of films and TV shows, not to mention the impact of the Internet, which has given a completely different meaning to the distribution of such multimodal products by rendering them global, unlimited and ubiquitous (see Doherty 2016). The rising forms of media production and distribution are affecting the AVT academia as well as the professionals (Díaz Cintas 2013) by creating novel research trajectories, new market opportunities, and rapid circulation of media products. ...
Compared to one-line subtitles, two-line subtitles are believed to receive more attention from viewers based on previous research. Yet, in the majority of these studies, two-liners are considerably longer than the one-line subtitles. The authors argue that the findings of the previous studies could have been affected by the difference in subtitle length, and there is a need to operationally distinguish between the impact of subtitle length and line number on viewers’ attention allocation. Therefore, an SMI eye tracker was used in this study to record the eye movements of 32 Iranian viewers while reading the Persian subtitles of a short segment of a feature film, A Prophet (Jacques Audiard 2009). The results showed that the viewers’ attention to one-line subtitles was significantly greater than the attention they allotted to two-line subtitles although they were of the same length. The attention allocated to the long subtitles was also significantly greater compared to the attention paid to the short subtitles. Retrospective interviews also showed that the participants favored short and two-line subtitles.
... This may not conform to the context of the given text to be translated (Bédard, 2000). Moreover, translators may use TM deliberately to reduce variance in the translation resulting in poor translation quality and inconsistency (Moorkens et al., 2014;Doherty, 2016). We circumvent this problem by allowing the user to incorporate domain-specific lexicons while producing raw MT. ...
We introduce UDAAN, an open-source post-editing tool that can reduce manual editing efforts to quickly produce publishable-standard documents in different languages. UDAAN has an end-to-end Machine Translation (MT) plus post-editing pipeline wherein users can upload a document to obtain raw MT output. Further, users can edit the raw translations using our tool. UDAAN offers several advantages: a) Domain-aware, vocabulary-based lexical constrained MT. b) source-target and target-target lexicon suggestions for users. Replacements are based on the source and target texts lexicon alignment. c) Suggestions for translations are based on logs created during user interaction. d) Source-target sentence alignment visualisation that reduces the cognitive load of users during editing. e) Translated outputs from our tool are available in multiple formats: docs, latex, and PDF. Although we limit our experiments to English-to-Hindi translation for the current study, our tool is independent of the source and target languages. Experimental results based on the usage of the tools and users feedback show that our tool speeds up the translation time approximately by a factor of three compared to the baseline method of translating documents from scratch.
Rate-setting is a problematic area for newcomers to translation and established practitioners alike. Survey data generally support the view that translators feel underpaid and that money matters remain a chief ethical and pragmatic concern, but appropriate guidance is almost entirely absent from introductory textbooks on the translation profession and documentation prepared by industry associations remains unsatisfactory. Focusing on the translation industry in the United Kingdom, this conceptual paper explores constraints that limit price formation practices, and argues that translators feel under threat from disruptive technologies, Uberisation, and non-professional translation, now more than ever. We explore the complex interaction between status, internal and external perceptions, and regulation, and illustrate their push-pull relationship with rate-setting within a range of industry ‘educators’, uncovering the ways in which translators themselves, translation associations, and academic institutions directly and indirectly impact upon rate-setting practices. The article concludes by considering potential channels to buoy status and improve rate-setting practices in the translation industry.
With the technological turn of applied research in translation, more and more attention has been paid to the teaching of translation technology. In order to answer these two questions, how to independently develop MTI translation teaching resources with ethnic minority characteristics? How to use information technology to carry out Tibet-related CAT teaching? This article discusses the background, structure and functions of the International Publicity Translation Corpus (IPT Corpus) for Tibetan Areas of China through empirical research, combining theory with practice, and validates the translation teaching mode through case study, so as to better train translators and interpreters related to Tibetan culture. Through teaching practice since 2017, MTI translation workshop based on IPT Corpus is proved to be an effective teaching mode, which is worthy of further improvement and extension.
Purpose The automation of libraries has made a significant difference in the quality of service. All libraries seek to improve the quality of their services, and the library seems to be a frequent target for modernization efforts throughout the globe. Local dialects need specific translations to use library management software. A variety of languages are available to create open-source techniques. The integrated library management system Koha is gaining traction. The Koha software has been translated and made accessible to a worldwide audience in many languages. Design/methodology/approach This experiment was based on a controlled setting and a realistic observation of the Koha translation process. A sample of a population is selected to study and draw conclusions about the entire population. Findings Results are based on a statistical report made available on the Koha translation site for the gathering of sample data, which is based on sample data that has been collected and brought to light. This analysis demonstrates how to translate Koha software on a pootle server step by step using sting data inputs on different po files accessible in translation databases. Originality/value The authors of this paper explain the procedure of translating Koha software and provide a global overview of Koha software translation into different languages throughout the world. Therefore, it can be expected that the importance of Koha software will increase tremendously for the people of different languages through the method of translation.
Full-text available
This introduction to the 10th issue of Linguistica Antverpiensia New Series – Themes in translation Studies (LANS-TTS) begins by discussing the central concept of community translation, highlighting its terminological ambiguity. This is in part due to the already well-established field of community interpreting where the term is often used to mean the written translation of public information for immigrants. It is also an indication of the terminological instability typical of an emerging paradigm. For example, community translation is used more or less synonymously with such terms as translation crowdsourcing, user-generated translation and collaborative translation. The meaning of the term as we discuss in this issue can be best specified when the concept is anchored in the context of Web 2.0 (second generation web-technologies). This in turn acknowledges its intrinsic tie to online communities and directs us to new dynamics resulting from general Internet users acting as translators. While participants in community translation are not necessarily all unpaid, untrained volunteers community translation is used by some organisations as a mechanism to obtain free translations by going outside the professional translation sphere. To this end the ethical question of profit-making enterprises accessing free labour on the pretext of openness and sharing remains. That said, the author believes community translation is far more than a dilettante, anti-professional movement. Building on the emerging picture from the contributions in this volume, the author suggests some of the future directions that research on community translation might take, emphasising the need to reflect on the current translation practices and be open to the new developments and opportunities arising from the free and social Internet.
The notion that “two heads are better than one” is hardly new when applied to translation. The entire corpus of Buddhist sutras was translated into Chinese collaboratively by foreign and Chinese monks over a thousand-year period which began in the 1st century A.D. (Chueung, 2006). However, the dominant model used today for translation in the commercial sector depends on a process that largely inhibits collaboration. This article presents some of the latest findings from research on the state of community translation, based on multiple market research studies carried out over a five-year period, including a comparative analysis of 100 community translation environments and interviews with stakeholders. The research reveals that, over the course of the last several years, translation industry participants have been moving away from the traditional process toward a more dynamic and collaborative model. As community-based models have grown in popularity, distinct types of environments have emerged as well.
Translation involves ethical decision-making in challenging contexts. Codes of practice help professional translators identify ethical issues and formulate appropriate, justifiable responses. However, new and growing forms of community translation operate outside the professional realm, and substantial differences exist between the two approaches. How relevant, then, are professional codes in the new contexts? What alternative ‘codes’ (stated or implicit) have been developed by the new groups? The content of professional codes is compared here to a broad range of community approaches to identify themes common across both, and areas where the new community might be making an original contribution. This reveals different priorities in the professional and non-professional codes. Community translation initiatives have found novel solutions to some ethical problems and challenges, particularly in self-regulation and community policing, improved interpretation of code content, an emphasis on shared values rather than individual rights, and strong mentoring.
This introductory text to statistical machine translation (SMT) provides all of the theories and methods needed to build a statistical machine translator, such as Google Language Tools and Babelfish. In general, statistical techniques allow automatic translation systems to be built quickly for any language-pair using only translated texts and generic software. With increasing globalization, statistical machine translation will be central to communication and commerce. Based on courses and tutorials, and classroom-tested globally, it is ideal for instruction or self-study, for advanced undergraduates and graduate students in computer science and/or computational linguistics, and researchers in natural language processing. The companion website provides open-source corpora and tool-kits.
The Oxford Handbook of Translation Studies covers the history of the theory and practice of translation from Cicero to the digital age. It examines all major processes of translation, offers critical accounts of research, and compares competing theoretical perspectives. It considers all kinds of translation from sacred texts, poetry, fiction, and sign language to remote, consecutive, and simultaneous interpretation in legal, diplomatic, and commercial contexts. The two opening parts of the book consider the history of translation theory and central concepts in the study of translation. Parts III, IV, and V cover the written text, the interpretation of speech and sign language, and the role of translation in mixed-mode and multimedia contexts. Part VI considers the contributions and challenges of information technology including the uses and limitations of machine technology. The final part looks at the teaching and training of translators and interpreters. The book concludes with a bibliography and index. © Editorial matter and organization 2011 Kirsten Malmkjær and Kevin Windle. All rights reserved.
This chapter seeks to shed light on the underlying implications of new technologies for translation studies. Here ‘new’ does not merely signify chronological order but is used in a similar manner as in ‘new media’, highlighting the novelty of their profound impact in engendering ‘new’ social practices (Flew 2008: 1–4). This means that the topic is not confined in scope to technologies that have emerged in the twenty-first century, nor to those in translators’ immediate surroundings; rather, the chapter attempts to provide a critical analysis of unfolding technological changes affecting translation in broader contexts.