Tag: MT


A New Translation App from…Hyundai?

AI, Business News, Language in Business, Localization, Localization Technology, Machine Learning, Technology, Translation, Translation Technology, Uncategorized

There’s a new Korean-English translation app out there and it’s made by Hyundai. That’s right: The automobile manufacturer has gotten in the language business.

According to newspaper The Korea Herald, AIRS Company — an artificial intelligence subsidiary of Hyundai Motor Group — developed the tool in order to help the corporation’s international employees communicate. Called the H-Translator, it uses an artificial neural network (ANN) to translate written text as well as extract copy from captured images.

The South Korea-based AIRS Company was established in 2018 and began building the app in 2019. H-Translator can be used both as a standalone tool and with external chat tools. The manufacturer is currently working on adding additional languages and wearable functionality.

In a statement posted on Facebook on December 17, AIRS Company used Facebook’s translation automation to announce the tool into languages other than Korean. In English, one of the more coherent portions read “From the world, we offer the highest level of translation quality specialized in the automotive industry. We are looking forward to being a beginner of translator development for communication between the future vehicles or robots, etc.”

Founded in 1967, Hyundai is a South Korean automotive manufacturer headquartered in Seoul. The company is no stranger to international business: it operates the world’s largest integrated automobile manufacturing facility in Ulsan, South Korea, and employs about 75,000 people all over the world. As Hyundai has noted, many employees don’t speak the same language. Hyundai vehicles are sold in 193 countries.

In September, Business Korea reported that Hyundai had recruited top AI scholars as part of a strategic transition to expand its offerings: “To pioneer the future of sustainable mobility, [Hyundai] is undergoing an ambitious transformation from an automotive manufacturer to a smart mobility solution provider and it is invested and engaged in various projects and collaborations covering AI, autonomous driving, electrification, and Mobility as a Service (MaaS).”

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MultiLingual creates go-to news and resources for language industry professionals.


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Lilt Hosts Successful Virtual Conference

Language Industry News and Events

Lilt, an AI-powered enterprise translation company, held its first Lilt Ascend virtual conference yesterday, October 22. The theme was localizing at scale in a digital world, a topic that underscores the challenge companies face as the volume of digital content continues to grow exponentially while the pool of linguistic talent remains constant.

The conference came right on the heels of Lilt becoming the first ever Diamond sponsor of the Women in Localization organization, which was announced earlier this week. And indeed, advancing the role of women in the industry was a recurring theme throughout the day’s sessions. The conference kicked off with a keynote speech by Lilt’s chief evangelist, Paula Shannon, who emphasized the benefits of finding a mentor and filling in gaps in financial literacy to those seeking more senior roles in the industry.

Following the introductions, Lilt’s CEO Spence Green gave a presentation on how Lilt is helping enterprises address the challenge of localizing digital content at scale. For those whose first introduction to Lilt was through their adaptive MT-enabled Translator Workbench, it was informative to hear about improvements in translation and review workflows. These include a Neural AutoReview feature that provides reviewers with context-based stylistic suggestions for text improvements. Green also announced several other new and improved services, including connectors to leading TMS solutions, an on-prem private cloud deployment option, and improved data modeling via Lilt Insights.

The conference was a well-balanced mix of product updates and demos, conversations with industry thought leaders on how they’ve driven digital transformation, and presentations on AI that ranged from the technical to the practical. Speakers from Intel, Aisics Digital, and Canva took the audience through challenges they faced scaling their localization programs, and how Lilt has helped them achieve the efficiency gains needed to keep up with their global customers. 

Lilt Ascend was hosted on Hopin, an interactive online event platform. As anyone who has attended virtual conferences in this brave new world knows, the platform can make or break an event. Hopin did not disappoint. Though there were a few minor delays lining up speakers for Q&A portions, the audiovisual quality was excellent and the UX was quite intuitive. The networking function also worked well, setting up participants with one-on-one sessions lasting 15 minutes each. Overall, it was a solid solution that any event organizer would be remiss not to consider.

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MultiLingual creates go-to news and resources for language industry professionals.


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GlobalLink AI Portal Delivers Over 1 Billion Words Per Month


Surpassing one billion words per month, TransPerfect’s GlobalLink AI Portal solution has doubled in usage this year, as TransPerfect signs on several new clients.

TransPerfect, one of the world’s largest providers of language and technology solutions for global business, announced this week that the adoption of its GlobalLink AI Portal machine translation (MT) solution for corporate clients has surpassed the one billion words per month milestone and continues to grow.

With the addition of new clients like Cushman & Wakefield, HARMAN International, and Cummins, GlobalLink AI Portal usage has more than doubled this year. Serving over 5,000 global organizations, GlobalLink Product Suite simplifies management of multilingual content. TransPerfect has seen long-term clients who use the service achieve an increase of 15% in quality scores over the previous year, demonstrating the AI solution’s capacity to continuously improve in a secure environment. The increased quality allows clients to reduce the scope and scale of necessary post-edits by streamlining time and cost.

Among TransPerfect’s numerous language and technology solutions, GlobalLink AI Portal focuses on real-time self-service MT and supports more than 40 different languages and 30 different file formats. The solution makes neural MT more accessible to corporate clients looking to integrate the technology into their business workflows. Offering a hybrid approach, TransPerfect combines AI and human translation to help clients achieve an optimal position on the quality-cost translation matrix for the content’s end use.

Furthermore, GlobalLink AI Portal offers unique security features that include the use of certified collocation facilities, encryption, secure HTTPS access, optional deactivation of data storage, single sign-on (SSO) integration, and user permissions and hierarchies.

TransPerfect President and CEO Phil Shawe said that “Efficiency and security are two of the pillars on which our company has operated for over 25 years. I’m happy to see the marketplace’s rapid adoption of our GlobalLink AI solution, but I’m even more pleased to know that we’ve delivered this technology in a way that both drives productivity and respects privacy.”

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MultiLingual creates go-to news and resources for language industry professionals.


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Gallaudet University Partners with Apple and AppTek

Business News, Technology

In a moment when universities need the latest technology more than ever, Gallaudet University has announced two important partnerships with Apple and AppTek, which aim to provide its deaf and hard-of-hearing students with tools necessary to succeed in an increasingly technological world.

As the fall term commences, Gallaudet University has announced a couple exciting pieces of news for its deaf and hard-of-hearing students. Gallaudet is a federally chartered private university for the education of the deaf and hard of hearing located in Washington, D.C. In a statement on Thursday morning, Gallaudet University President Roberta J. Cordano announced that the university would begin a partnership with Apple to improve access and expand academic and career opportunities for Gallaudet students.

In her statement, the president said, “Beginning this fall, Gallaudet will provide all students and faculty with an iPad Pro, Apple Pencil, and SmartFolio for iPad Pro to support their learning and teaching. Students and teachers at the Laurent Clerc National Deaf Education Center will also participate in this new initiative.”

Providing students better access to the most up-to-date technology, the partnership will also establish an Apple scholarship program for students of color with disabilities. The scholarship will go to students pursuing studies in information technology, computer science, and other science, technology, and math related fields.

“Gallaudet has been at the forefront of advancing education and acceptance of Deaf culture in this country for more than 150 years,” said Lisa Jackson, Apple’s vice president of Environment, Policy and Social Initiatives. “We are honored to work together with this incredible institution to create even more opportunities for Gallaudet students and for all underserved and underrepresented communities.”

Furthermore, through the Connected Gallaudet initiative, Gallaudet students will participate in research projects to design bilingual applications. One project in particular was also announced this week, which revealed a partnership between Gallaudet University and AppTek, a leader in artificial intelligence (AI) and machine learning (ML) for automatic speech recognition (ASR) and machine translation (MT).

This application aims to provide video conference participants with live closed captions and deliver more control of the user interface (UI), allowing users to enhance the readability of real-time conversation transcripts and enjoy a more meaningful flow of spoken content.

“While much of the world is relying heavily on videoconferencing applications to communicate safely during the COVID-19 pandemic, commonly used applications unfortunately do not provide reliable, real-time capabilities that allow deaf and hard of hearing participants to engage fully,” said Mike Veronis, AppTek Chief Revenue Officer and Program Manager for the 21st Century Closed Captioning project. “We are passionate about and humbled at the opportunity to collaborate with Gallaudet on bridging that gap by developing new tools to give the deaf community greater freedom, control, and access to virtual communication.”

Integrating AppTek’s ASR platform, the application will incorporate the latest AI and ML technologies to enable this assistive service, which will be available to users on demand. Over time, Gallaudet also intends to incorporate multilingual capabilities using AppTek’s Multilingual Automatic Speech Recognition and Neural Machine Translation technologies.

Along with new technology and the application development project, Gallaudet University will also grant some students the opportunity to take part in the Apple Worldwide Developers Conference (AWDC) through the partnership with Apple. The annual event brings together over 5,000 developers, innovators, and entrepreneurs for engineering sessions, forums, laboratories, and keynote presentations about the latest app and software innovation.

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Journalist at MultiLingual Magazine | + posts

Jonathan Pyner is a poet, freelance writer, and translator. He has worked as an educator for nearly a decade in the US and Taiwan, and he recently completed a master’s of fine arts in creative writing.


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Translated to provide EU Parliament with real-time speech translation AI

AI, Business News

Debates will be transcribed and translated by a new state-of-the-art MT system that keeps humans in the loop

blue and white flags on poleTranslated has been selected by the European Parliament to automatically transcribe and translate parliamentary multilingual debates in real-time, covering the 24 official languages used by the institution. The service will be provided by new software available both through fully-localized web and mobile applications, and live streaming APIs for third-party developers. It it purported to be the first human-in-the-loop speech machine translation (MT) system, and should leverage context and user feedback to adapt the output in less than one second.

The product will be developed in collaboration with two companies that have already worked with Translated in building products for professional translators: Fondazione Bruno Kessler (FBK), a world-leading research center in MT and automatic speech recognition (ASR); and PerVoice, an ASR world-leading provider. Within the next 12 months, the consortium will release a prototype to be tested by the European Parliament. This solution will be considered alongside solutions provided by two other groups, following rules put forth in “Live Speech to Text and Machine Translation Tool for 24 Languages.” The best-performing tool will be confirmed as the official one for the following two years.

The new product is not a simple concatenation of ASR and MT, but a new, fully-integrated system in which the MT algorithms are tolerant of ASR errors. This approach will not only help deliver more contextualized translations, but it will also open up the opportunity to improve the quality of the output while the plenary session is happening. This is possible thanks to the human correction feedback that the tool allows by both the end-users and a team of professional translators.

“For this project, we are bringing together ten years of research in machine translation and speech recognition,” says Simone Perone, Translated’s vice president of product management. Some of the new AI models that will be used have already been put to work successfully in products such as ModernMT (an MT that improves from corrections and adapts to the context), Matecat (a computer-assisted translation tool that makes post-editing easy), and Matesub (the first subtitling tool offering suggestions during the transcription, now in beta and due to be released in September 2020).

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In Technology, Things Never Slow Down

Translation Technology

I am of an age where I can recall the pre-email intra- and extra-office communications process. Both were served by what is now called snail mail. External communications got a stamp and were posted at the end of every day. Internal communications involved a dedicated person traveling around the offices handing out memos in large brown envelopes tied with string. If you were on the recipients’ list, the brown envelop was placed in your In-tray by the office postal clerk for you to peruse, at your leisure.

Once you had read the contents, and added a note of comment to them, they were then placed in your Out-tray. You ticked off your signature to show that you had read the contents. The brown envelop was then moved on by the clerk who spent his day walking the corridors to carry out this vital task.

You know what, the process worked — albeit at the pace of a crocked snail. But that’s how the world was back then. People neither expected nor demand things to be addressed immediately.

Then some office IT genius spotted this new technological advancement that was sweeping the world. It was called email. The technology was duly introduced and we all received training on how this new-fangled invention worked. The old brown envelopes disappeared and the postal clerk put on a lot of weight from lack of exercise. But for worse (or for better?), the pace of work in the office was ramped up immeasurably. Suddenly messages were being received in your electronic In-tray and expectations grew that a message received should be answered immediately, if not sooner. Decision-making became a nanosecond exercise.

Indeed, people sitting only feet from you would “ping” (that was a new word for us) an email to you, rather than simply shout across the office or talk to you over the water cooler. The introduction of this new technological changed the face and the pace of every office. It put it in to an overdrive that it never really decelerated from. I tell you this “All of Our Yesterdays” anecdote by way of demonstrating to you how technology begets a change that is often one of speeding up processes. Seldom does new technology aim to slow things down.

This speeding up is being driven by the constant evolution and improvement in the capacity of computers to crunch and process data. As the physical hardware gains more computational power, with super processing chips, that power is used to process and spit out huge corpora of data at breathtakingly fast speeds. But even this power is not proving sufficient as companies hunger for faster and cheaper solutions to their growing need to process huge amounts of data at almost real-time speeds. Already research is at an advanced stage whereby the silicon chip will be replaced by a new technology called the carbon nanotube. And on and on it will go.

Neural machine translation evolution

The evolution of neural machine translation (NMT) too has been evolving at a breakneck pace. NMT development has moved at five times the pace of earlier statistical machine translation (SMT) research, and the developments in industry bear this out. Google replaced a system they had developed over the course of 12 years with a new NMT system they developed in just over 18 months. With these developments comes the improvement of outcomes and capabilities. The rapid evolution of NMT has been served by the huge amount of time and effort being put in to research by many of the giants of industry. This factor, married to the development of faster and affordable hardware, has facilitated the ongoing demands for more speed and computational power. Google is working with a start-up company called Nervana Systems that is developing the Nervana Engine, an ASIC processor that increase current processing speeds by a factor of 10. Not surprisingly, Nervana Systems was bought by Intel in 2016.

It is no surprise that NMT, which is a model inspired by the workings of the human brain, is greedy for the speedy processing of huge corpora of complex data. And it is a sobering thought that the average human brain processes data at 30 times the speed of the best supercomputers. Fortunately, with the advance of Deep Learning, SMT requires only a fraction of the memory needed for traditional SMT. Whereby Email was demanded because the world needed to speed up inter- and extra-office communications, the development of NMT is being driven by the proliferation of mobile devices, in-home control systems, the rise of social media and the demand for real-time communications, the growth of e-commerce as a market opportunity for companies and the growth of Big Data and its insatiable appetite to crunch and understand huge amounts of data now, in multiple languages and at an affordable cost.

The adoption of NMT by behemoths such as Google has meant that this language solution has been given the blessing that it is a technology worthy of investment and research. And as is the way in industry once one giant adopts a system the other equally powerful entities feel the need to develop their systems. Facebook too has joined this race. Indeed, the top companies in the world, including Microsoft, Google, Amazon, eBay and Facebook to name but a few, have ongoing investment and research in NMT. With R&D spending prowess of these companies it is no wonder that the development of NMT has gathered such a pace. In fact, NMT is expected to surpass all other MT models and to grow to a market share of $46 billion by 2023.

The objective of NMT development is no small one. In essence, it can be defined as advancing a system that will allow people from anywhere in the world to be able to connect with anyone, and understand anything in their own language. Add to that the need for quality and speed and you can see the mountain NMT has to climb, and has been successfully climbing. Yet achievement of that objective is getting closer. Google, for example, supports 103 languages, it translates a 100 billion words per day (you read that right!) and communicates with the 92 percent of its users who are outside of the USA.

Those are staggering figures. But if companies want to grow their brands, open up fertile new markets and keep their shareholders happy, then these are the levels that must reach to keep pace with developments in NMT. And we are not only referring to the written word, for more and more of the demands are for the spoken word with the growth of voice activated technology and household “gadgets” such as Amazon’s Alexa, Google’s Home and Apple’s HomePod (and that list is growing). And the future of NMT is further being cemented by its adoption by key industries such as Military & Defence, IT, Electronics, Automotive and Healthcare to name just a few.

NMT has now been taken up by all serious language service providers (LSPs). The debate is ongoing as to how this will impact on the current LSP model. Undoubtedly, the role of the human translator is evolving to one of being an editor rather than translator. Pricing models are changing from the traditional price per word based on word volumes, to pricing on a time-measured rate. An expert at eBay has predicted that the traditional translator will evolve to become “… date curators of corpora for MT.” Our founder Tony O’Dowd has a bleaker assessment for the human translator when he says, “the traditional approach to translation is dead (or in its twilight zone).” But one thing seems sure, NMT — like email — is not going to go away. Speed is of the essence. That is the eternal watch-cry of technology.

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Aidan Collins is a language industry veteran. He began his localization career as desktop publishing manager in Softrans (later bought by Berlitz) in 1991. In the following years, he has held senior management positions in both major LSPs and global technology companies. He is currently marketing manager with KantanMT.


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Linguistic prejudice, race and machine translation


Linguistic prejudice

Linguistic prejudice, race and machine translation

There are two basic approaches to grammar: the kind that says “this is what the rule book has said since 1858” and the kind that says “language evolves, and this is how it’s actually being used in the current world to communicate these specific concepts and grammatical differences.” The way pockets of minority speakers use language has always fascinated me, although when I was young it would make me cringe. As a teenager, I thought it was extremely strange, for example, that the black-cap Mennonite community that I sometimes mingled with used word constructions I’d never heard of in real life; they greeted me with “welcome here” instead of “hi;” their pronunciation of “school” sounded more like “skewel.” It sounded super-archaic to me, like in eschewing modern forms of dress they’d also decided — subconsciously or consciously — to eschew modern linguistic constructions.

During grad school, one of my linguistics professors delved into the linguistic nuances of African-American Vernacular English (AAVE). He told us that there was a grammatical dialectical difference between “this coffee cold” and “this coffee be cold” and the difference did not exist in standard American English. “This coffee cold” was a remark about a temporal state; “this coffee be cold” was a remark about a known, habitual quality of this specific genre of coffee. Similar to “le café a été froid” and “le café était froid,” I imagine, or perhaps more accurately, “this coffee is cold” and “this coffee is usually cold.”

AAVE drops certain sounds (but not others) in spoken language; there’s a regularity to the practice. Because there are rules, this is no more “incorrect” in English than when it happens in French or in certain dialects of Spanish; Cuban Spanish, for example, may also drop sounds with a practiced regularity. AAVE drops “to be” verbs in some instances, but then, so does standard Hebrew and Russian. Standard English drops the verb in phrases such as “every man an island unto himself.” White dialectical English drops it in phrases such as “this floor needs swept.”

In short, contrary to the opinions of grumpy white grammar nazis, AAVE isn’t “wrong,” it just does its own thing, having adapted the way language always adapts. And this is important because for a certain portion of the population, these grammatical differences become a reason to mistrust African-Americans, to dismiss them as “uneducated” or “lazy” because they sound different. To treat them with less innate respect.

For a certain portion of the population, these grammatical differences become a reason to mistrust African-Americans, to dismiss them as “uneducated” or “lazy” because they sound different. Click To Tweet

A study put out in June of this year, for example, concluded that police use less respectful language with black members of the community than white members of the community, even controlling for heavy-crime areas and reasons for the police stop. The study could find no difference, in fact, than the race of the people being spoken to by police.

Now, I find it hard to believe that the majority of police are linguistically reacting purely to the skin color of the person in front of them. What seems more likely to me, as a linguist, is that they react linguistically to linguistic difference (real or perceived). When a person speaks a non-standard dialect, or is assumed to speak a non-standard dialect, that person is usually placed in a more suspect category. If their speech itself is not “correct,” what else is not correct about them?

I consider myself open-minded on such matters, but I am by no means immune to this. I noticed as I was recently watching an interview with Seattle Seahawks-turned-Oakland Raiders player Marshawn Lynch that I couldn’t stop the subconscious commentary in the back of my head on his pronunciation of ask as “axe,” or the myriad of ways he sounded like a stereotypical black man. His way of speaking sounded incorrect to my brain; the unintentional emotional result ranged between slight irritation and amusement. Neither are particularly respectful reactions. The guy standing next to me, on the other hand, remarked “I love that he’s himself, and he isn’t dumbing himself down for the media. He sounds so black. He’s such a badass.”

This guy had grown up siding with his black friends against stereotypical white jock bullies and Klansmen in the south, so his firsthand experience with African-American dialects was way more intimate than mine. More friendly, more familiar. His subconscious was trained differently than mine.

And I thought, you know, he’s totally right. It’s pretty badass that this guy is refusing to change who he is, refusing to give up his linguistic heritage, in the pursuit of fame or being more palatable to the money machine of corporate America.

I posit that, given my own reaction, white Americans are less likely to believe a man committed a crime if he sounds like them; if he speaks with the cadence and vocabulary of a white man. This is, of course, a difficult theory to prove in a double-blind study, but it bears out anecdotally. As this study shows, it is true that many people are implicitly biased against accents unlike their own and certain accents in particular, whether or not they realize it. It is also true, for example, that all-white juries are 16% more likely to convict a black defendant than a white defendant.

It seems likely that linguistics play a role, and they certainly have on a trial-by-trial basis. After Trayvon Martin was fatally shot in Sanford, Florida, by George Zimmerman, Martin’s friend Rachel Jeantel, who had been present, testified against Zimmerman. Jeantel spoke non-standard English. Her speech patterns were widely mocked on social media, while her testimony was ignored by jurors. A prize-winning linguistics write-up put it this way: “one of the six jurors (B37) said, in a TV interview with CNN’s Anderson Cooper after the trial (July 15, 2013), that she found Jeantel both ‘hard to understand’ and ‘not credible’. In the end, despite her centrality to the case, ‘no one mentioned Jeantel in [16+ hour] jury deliberations. Her testimony played no role whatsoever in their decision’ (Juror Maddy, as reported in Bloom 2014:148). In a sense, “Jeantel’s dialect was found guilty as a prelude to and contributing element in Zimmerman’s acquittal.”

Accent and dialect influence how you’re perceived. I once conducted my own experiment on accent: during my first semester of grad school, I was employed taking phone surveys about Charmin Ultra toilet paper. This was extremely boring, so I ran my own secondary experiment in the background: I would alternate calls in an Irish accent, in a standard American accent, and in a Southern accent. I was curious if accent played any role in people’s willingness to take a survey about toilet paper; the majority of people hung up on any accent, but maybe there was a competitive edge I could use to complete more surveys, and thus to earn more money per hour.

I was calling non-Southern white Americans, by the sound of it; the call’s geography was random numbers pulled from somewhere like northern Arizona or Wyoming. I kept track of completed surveys in each accent. After doing this enough times, a pattern started to emerge: people slightly preferred talking to a woman who spoke in a soft-and-subtle Irish accent, followed by standard, crisp American English; Southern American English was a distant last. Few people seemed to take Southern Accent Girl seriously enough to complete a toilet paper survey with her voice on the other end of the line.

Southern accents are often associated with being “uneducated” or “dumb,” even to listeners as young as five years old, so this was not a huge surprise. And what American, on the other hand, doesn’t love the Irish?

And lest this be considered an American phenomenon, British studies have found that speaking with a Birmingham accent (like Ozzy Osborne) makes listeners assume that you are less intelligent compared to standard British English or a Yorkshire accent.

Humans make judgments about dialect and language, often without realizing it. However, machines only do this where their data is prejudiced in some way. Data-driven linguistic models collect data removed of innate prejudice, studying how humans use language and deriving rules from this. Although this has certainly resulted in subtle and non-subtle human prejudice being codified into machine learning, it also presents an opportunity to create programs that may correct for human prejudices. Data-driven models often work best when the linguistic field is narrowed, actually, because human language is so broad. Because of this, I wonder if there will be — could be — machine translation settings in the future that take into account the dialect of English being spoken; certainly this is a question that speech-to-text MT applications have to take into account. And I wonder if this, somehow, could be used to “translate” dialects in places like the courtroom, for the benefit of everyone.

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Katie Botkin, Editor-in-Chief at MultiLingual, has a background in linguistics and journalism. She began publishing "multilingual" newsletters at the age of 15, and went on to invest her college and post-graduate career in language learning, teaching and writing. She has extensive experience with niche American microcultures across the political spectrum.


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Common Sense Advice about Machine Translation and Content

Translation Technology

You’d need to be living on the moon if you still don’t get it about how data quality impacts machine translation quality (actually, every kind of translation). But, what does this fact really mean when communicating with content creators?

Writers, and information developers generally, have to contend with all sorts of “guidance” about how they must create content to make it easily “translatable”. I am against that sort of positioning.

Content creators need and want guidance on how to make their content usable, not translatable. There is no conflict between making content readable in English and making it easily translatable, and vice-versa. There is a conflict between telling content creators to make their content translatable and not accounting for content style, source user experience, and especially the motivations and goals of the content creators themselves.

Well, I have been reading the Microsoft Manual of Style (4th Edition), recently published, and I am delighted to see there is a section called “Machine Translation Syntax”.

Microsoft Manual of Style 4th Edition. Sensible stuff about machine translation.

Microsoft Manual of Style 4th Edition. Sensible stuff about machine translation. Did I mention that I got a new bag from Acrolinx?

Here is what that section says:

“The style of the source language has significant impact on the quality of the translation and how well the translated content can be understood.”

The style of the source language. Brilliant appeal to the audience! What follows is a baloney-free set of 10 guidelines for content creators. Each guideline appears to be an eminently sensible content creation principle worth respecting, regardless of the type of translation technology being used, or even if the content is not explicitly destined for translation at the time of creation.

You can read the 10 guidelines on the Microsoft Press blog.

Well done Microsoft, again (no, I am not looking for a job). Let’s see more of this kind of thing from everyone!

I’ll do a review of my new Acrolinx bag when time allows.

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Ultan Ó Broin (@localization), is an independent UX consultant. With three decades of UX and L10n experience and outreach, he specializes in helping people ensure their global digital transformation makes sense culturally and also reflects how users behave locally.

Any views expressed are his own. Especially the ones you agree with.


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