Tag: Translation Project Management

Files, Files Everywhere: The Subtle Power of Translation Alignment

Files, Files Everywhere: The Subtle Power of Translation Alignment

Here’s the basic scenario: you have the translated versions of your documents, but the translation wasn’t performed in a CAT tool and you have to build a translation memory because these documents need to be updated or changed across the languages, you want to retain the existing elements, style and terminology, and you have integrated CAT technology in your processes in the meantime. The solution is a neat piece of language engineering called translation alignment.

Translation alignment is a native feature of most productivity tools for computer-assisted translation, but its application in real life is limited to very specific situations, so even the language professionals rarely have an opportunity to use it. However, these situations do happen once in while and when they do, alignment usually comes as a trusty solution for process optimization. We will take a look at two actual cases to show you what exactly it does.

Example No. 1: A simple case

Project outline:

Three Word documents previously translated to one language, totaling 6000 unweighted words. Two new documents totaling around 2500 words that feature certain elements of the existing files and need to follow the existing style and terminology.

Project execution:

Since the translated documents were properly formatted and there were no layout issues, the alignment process was completed almost instantly. The software was able to segmentize the source files and we matched the translated segments, with some minor tweaking of segmentation. We then built a translation memory from those matched segments and added the new files to the project.

The result:

Thanks to the created translation assets, the final wordcount of the new content was around 1500 and our linguists were able to produce translation in accordance with the previously established style and terminology. The assets were preserved for use on future projects.

Example No.2: An extreme case of multilingual alignment

Project outline:

In one of our projects we had to develop translation assets in four language pairs, totaling roughly 30k words per language. The source materials were expanded with new content totaling about 20k words unweighted and the language assets had to be developed both to retain the existing style and terminology solution and to help the client switch to a new CAT platform.

Project execution:

Unfortunately, there was no workaround for ploughing through dozens of files, but once we organized the materials we could proceed to the alignment phase. Since these files were localized and some parts were even transcreated to match the target cultures, which also included changes in layout and differences in content, we knew that alignment was not going to be fully automated.

This is why native linguists in these languages performed the translation alignment and communicated with the client and the content producer during this phase. While this slowed the process a bit, it ultimately yielded the best results possible.

We then exported the created translation memory in the cross-platform TMXformat that allowed use in different CAT tools and the alignment phase was finished.

The result:

With the TM applied, the weighted volume of new content was around 7k words. Our linguists localized the new materials in accordance with the existing conventions in the new CAT platform and the translation assets were saved for future use.

Wrap up

In both cases, translation alignment enabled us to reduce the volume of the new content for translation and localization and ensure stylistic and lexical consistencywith the previously translated materials. It also provided an additional, real-time quality control and helped our linguists produce a better translation in less time.

Translation alignment is not an everyday operation, but it is good to know that when it is called to deliver the goods, this is exactly what it does.

Reference: https://bit.ly/2p5aYr0

The Augmented Translator

The Augmented Translator

The idea that robots are taking over human jobs is by no means a new one. Over the last century, the automation of tasks has done everything from making a farmer’s job easier with tractors to replacing the need for cashiers with self-serve kiosks. More recently, as machines are getting smarter, discussion has shifted to the topic of robots taking over more skilled positions, namely that of a translator.

A simple search on the question-and-answer site Quora reveals dozens of inquiries on this very issue. While a recent survey shows that AI experts predict that robots will take over the task of translating languages by 2024. Everyone wants to know if they’ll be replaced by a machine and more importantly, when will that happen?

“I’m not worried about it happening in my lifetime” translator, Lizajoy Morales, told me when I asked if she was afraid of losing her job to a machine. This same sentiment echoes with most of Lilt’s users. Of course, this demographic is already using artificial intelligence to their advantage and tend to see the benefits over than the drawbacks.

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Many translators, however, are quick to argue that certain types of content are impossible to be translated accurately by a machine, such as literature, which relies on a human’s understanding of nuance to capture the author’s intention. Or in fields like legal or medicine, that rely on the accuracy of a human translator.

But even in these highly-specialized fields, machines can find their place in the translation workflow. Not as a replacement, but rather as an assistant. As translators, we can use machines to our advantage, to work better and faster.

But I’m not talking about post-editing of machine translation. In a recent article from a colleague, Greg Rosner talks of the comparison of post-editing to the job of a janitor — just cleaning up a mess. True machine assistance augments the translator’s existing abilities and knowledge, letting them have the freedom to do what they do best — translate — and keeping interference to a minimum.

So how do machines help translators exactly? With an interactive, adaptive machine translation, such as that found in Lilt, the system learns in real-time from human feedback and/or existing translation memory data. This means that as a translator is working, the machine is getting to know their content, style and preferences and thus adapting to this unique translator/content combination. This adaptation allows the system to progressively provide better suggestions to human translators, and higher quality for fully automatic translation. In basic terms, it’s making translators faster and better.

Morales also pointed out another little-known benefit from machine translation suggestions: an increase in creativity. “This is an unexpected and much-appreciated benefit. I do all kinds of translations, from tourism, wine, gastronomy, history, social sciences, financial, legal, technical, marketing, gray literature, even poetry on occasion. And Lilt gives me fantastic and creative suggestions. They don’t always work, of course, but every so often the suggestion is absolutely better than anything I could have come up with on my own without spending precious minutes searching through the thesaurus…once again, saving me time and effort.”

Many are also finding that with increased productivity, comes increased free time. Ever wish there were more hours in the day? If you’re a translator, machine assistance may be the solution.

David Creuze, a freelance translator, told us how he spends his extra time, “I have two young children, and to be able to compress my work time from 6 or 7 hours (a normal day before their birth) to 4 hours a day, without sacrificing quality, is awesome.”

With these types of benefits at our fingertips, we should stop worrying about machines taking the jobs of translators and focus on using the machine to our advantage, to work better and ultimately focus on what we do best: being human.

 

Reference: https://bit.ly/2MgDaAj

What do You Want to See?

What do You Want to See?

Dears,

Thanks for visiting our blog!

Please share with us what do you want to see in our blog during the next period and what are the topics that interest you?

Thanks!

A Beginner’s Guide to Machine Translation

A Beginner’s Guide to Machine Translation

What is Machine Translation?

Machine translation (MT) is automated translation by computer software. MT can be used to translate entire texts without any human input, or can be used alongside human translators. The concept of MT started gaining traction in the early 50s, and has come a long way since. Many used to consider MT an inadequate alternative to human translators, but as the technology has advanced, more and more companies are turning to MT to aid human translators and optimize the localization process.

How Does Machine Translation Work?

Well, that depends on the type of machine translation engine. There are several different kinds of MT software which work in different ways. We will introduce Rule-based, Statistical, and Neural.

Rule-based machine translation (RBMT) is the forefather of MT software. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.

Statistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two millions words are needed to train the engine for a specific domain – which can be time and resource intensive. When a using domain specific data, SMT can produce good quality translations, especially in the technical, medical, and financial field.

Neural MT (NMT) is a new approach which is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack behind previous approaches when it comes to translating rare words and terminology. Long and/or complex sentences are still an issue even for state-of-the-art NMT systems.

The Pros and Cons of Machine Translation

So now you have a brief understanding of MT – but what does it mean for your translation workflow? How does it benefit you?

  • MT is incredibly fast and can translate thousands of words per minute.
  • It can translate into multiple languages at once which drastically reduces the amount of manpower needed.
  • Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.
  • MT technology is developing rapidly, and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.

There are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything. Sometimes MT alone is suitable, while others a combination of MT and human translation is best. Sometimes it is not suitable at all. MT is not a one-size-fits-all translation solution.

When Should You Use Machine Translation?

When translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.

For large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, MT can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.

Using a combination of MT along with a human translator post-editor opens the doors to a wider variety of suitable content.

Which MT Engine Should You Use?

Not all MT engines are created equal, but there is no specific MT engine for a specific kind of content. Publicly available MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content types.

Ultimately, choosing an MT engine is a process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.

Using Machine Translation and a Translation Management System

You can use MT on its own, but to get the maximum benefits we suggest integrating it with a TMS. With these technologies integrated, you will be able to leverage additional tools such as translation memories, term bases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine.

Reference: http://bit.ly/2P85d7P

Why People Leave the Language Industry

Why People Leave the Language Industry

Working in a language service provider (LSP) can be an exciting and challenging place to be: managing complex projects, figuring out best workflows, delivering to demanding deadlines, working with teams of people from diverse cultural and linguistic backgrounds… But what motivates people who have studied languages and translation to leave behind a career in the language industry? And where are they now?

Slator conducted a number of interviews and spoke to individuals who had all worked for 3+ years in a sales or operational capacity within a language service provider in either France or the UK. All had studied languages and / or translation to degree or post-grad level.

Respondents revealed that they were originally attracted to the language industry for a variety of reasons, including the appeal of a “multicultural environment” coupled with a “passion for languages”, “the ability to be able to communicate without borders”, having the opportunity to use languages on a daily basis and “learning more about…the art of translation and culture surrounding the language.”

The aspects of their roles that they enjoyed most ranged from “the camaraderie and internationalism” to “client side, travel and coaching people” and the fact that “it was always busy and fast-paced which kept me on my toes.”

International Environment

Asked what they liked about the companies (LSPs) they worked in, one respondent identified “the feel you were doing something important for large clients” and another said that she had worked in “a small company and became friends with colleagues”. Again, “international environment” and “international focus with different office locations” featured prominently.

On the benefits of the industry as a whole, one respondent said she “learned a lot about finance and legal, practicing at least two languages daily”. Innovation and technology also contributed to the appeal: “it is forever evolving and has an exciting future ahead,” one said, while another thought that “interesting technology advances” were a distinct positive.

So, despite the lure of an international and evolving market, where you can meet people and travel, what factors are leading individuals to leave the industry in favor of alternative careers? And what opportunities are they seeking out?

Why People Leave

We asked respondents to describe their reasons for leaving the language industry in their own words. One person, who worked for six years in project management, said that it was “impossible to progress, [there was] no reward for going the extra mile, [and] no human contact”.

Another, who spent three years in the industry in a project management role echoed this sentiment, saying that “I found I’d reached a plateau, both in terms of pay and also in terms of what my job entailed (it became quite samey)”. She went on to say “I figured my skills could be transferred into another sector which could offer more variety and options for the future”.

60% of respondents cited lack of progression for the primary reason for leaving.

One individual saw few differentiating factors between LSPs saying “I felt that most LSPs were much of the same and there was little difference between being a project manager at any of them. I decided to leave to travel for a few months and didn’t feel like I wanted to return to the same career.” Yet another thought that “the progression was too slow. Individuals were being overloaded and therefore project standards were slipping.” One person identified a culture of blame in the company he left behind to pursue a teaching career.

Only one respondent gave reasons that were uniquely “pull factors” and said that she had left “to pursue other avenues in a different field and focus on the transferable skills gained during my time at the various LSPs, notably, leadership and coaching” after 13 years in the industry.

When pressed, 60% of respondents cited lack of progression for the primary reason for leaving, with comments including “impossible to progress”, no “options for career progression” and “being overlooked for promotion”.

Stepping Stone to Another Career

A few interview questions centered on the respondents’ experience of pursuing a new career – how easy did they find it to secure a new role and how had working the language industry prepared them for a new line of work.

Most (80%) felt that the skills they had acquired were relevant to other industries. And the respondents had moved into a variety of roles and industries, from HR and Recruitment to Wine Buying, Events Coordination for a cloud networking company, Supply / Logistics, Marketing and Teaching.

Most found it “quick”, “easy” or “relatively easy” to find a new role, suggesting that language industry leavers are in high demand across a wide range of industries.

Most (80%) do not regret the decision to leave, while 20% are unsure (perhaps because it is still early days), but none are openly dissatisfied with their choice to pursue a different career. Similarly, none could foresee returning to the industry out of choice, e.g. “I hope it does not happen, but if it does, it will be for a short time as I am not willing to stay in this industry.”

The exception to this seemed to be the possibility of returning in the capacity of a freelance linguist: “The flexibility of being a freelance translator might tempt me at some point in the future. (I still do some freelance translating on the side of my current job)” and “If there was a feasible option of getting into interpreting I’d also be tempted.”

What Can We Do Better?

In a tick box question, respondents selected the factors that would have led them to stay in the language industry. A more interesting role and better long-term career opportunities topped the list, with more money, and better progression, management and work-life balance also emerging as possible difference-makers.

Participants were also asked what advice and recommendations they would have for employers in the language industry through questions such as “If you could change one thing about the language industry what would it be?” and “What, if anything, could your company could have done differently that would have encouraged you to stay in the language industry?”

Of the respondents who said that there was something their company could have done to retain them (60%), all said it would have involved better career developments or pay.

Things that people hoped to change about the industry at large included:

  • “Better salaries compared to Project Managers in other industries”;
  • “Make its employees feel more valued. The turnover was very high as extra duties were heaped on with no reward”;
  • “Make people more aware of how much work is behind a simple translated document, have respect for others’ job/work”;
  • “More honesty around quality assurance”

As the industry matures with a wider breadth of job roles and shows healthy levels of employment globally, companies must pay attention not to lose key talent to competing industries. Some might argue that LSPs are necessarily bottom heavy and that attrition among the more junior levels of project managers and sales employees is to be expected as it is not possible for everyone to progress. But turnover of staff carries a huge cost – not least for training, recruitment and extra overtime – one that can be driven down by heeding advice and implementing good people management strategies.

Reference: http://bit.ly/2LPiFhS

The Translation Industry’s Top-Earning Career Paths

The Translation Industry’s Top-Earning Career Paths

Adaptive’s recruiters are often asked by candidates how they can build their careers to raise their market value and earnings. Here we share our map of the paths which lead to some of the top-paying roles in the global language services industry.

First things first – you don’t have to be a salesperson to make big bucks.

Often when Adaptive is approached by candidates looking to up their earnings in the language services industry, there’s an expectation that only the high-flying BDMs and C-suite management are making top money.

After all, BDMs are on commission plans and signing big customer deals can be very lucrative. And it’s true – top BDMs and sales managers can be making as much as anyone on this list.

But salespeople are not the only ones with strong pay packages in the language industry.

In fact, we’ve left sales out of our list below to offer alternate options to translation and localization industry professionals looking to build their careers.

So here we go – four routes to top-paying roles if cold calling isn’t your thing:

1. Business Unit Leadership

e.g. VP Life Sciences, VP Engineering

Broad-ranging VP titles usually signify a role that is a mix of client relations, operations and specific expertise in a particular area.

Professionals in these positions are in charge of ‘business units’ which operate like mini-companies within the larger organization, focused on one specific area – such as services to the Life Sciences market or engineering services.

This means the VP’s responsibility is wide, often covering a separate profit and loss account for their unit. VPs leading these areas can come from a variety of backgrounds, but have often worked their way up an internal hierarchy where their increasing experience makes them more and more valuable.

They head up hiring, account management and ensure that their company’s service offering continues to be competitive and evolve with the market.

Career Entry Point: Project Manager, Account Manager, BDM

Key Skill: ability to combine rounded business skills with deep subject-matter expertise

Average Salary Range: $100,000 – $160,000 + bonus

2. Internal Technology Management

e.g. CTO, VP of Technology

At the highest level, technology managers need to be more than just experts in localization workflows, and lead areas such as networking, security, compliance, training, technology change management, data recovery and more.

Their focus is on the role technology plays in helping the company reach strategic goals and impacting overall P&L.

Localization career paths typically go from specialist to generalist with candidates building a base in CAT tools, internal and client workflows and then rounding out generalist IT competencies to continue progressing.

Career Entry Point: CAT Tools Specialist, Localization Technology Manager, Localization Engineer

Average Salary Range: $120,000 – $180,000 + bonus

Key skill: ability to visualize and implement technology changes which make high-value improvements to the global organization

3. Operations & General Management

e.g. VP Operations, General Manager

A great goal for Project Managers!

Many of the industry’s top-paid professionals in operations (production) leadership started ‘in the trenches’ as PMs. Growth in this career channel comes from deep first-hand knowledge of internal workflows, aptitude for working directly with key customers and versatile operational skills – organization, planning, financial management and personnel leadership.

As operations candidates move up the career ladder, they broaden their generalist business skills and combine them with their expert knowledge of localization processes to eventually step up and take overall responsibility.

Career Entry Point: Project Manager, QA Manager

Salary range: $120,000 – $150,000 + bonus

Key skill: ability to design and maintain efficient teams and workflows to deliver reliably and profitably for customers

4. Client Solutions Development

e.g. VP Client Solutions, Global Solutions Manager

A specialist team within most LSPs, solutions professionals focus on bridging the gap between sales, production and IT.

Their focus is building creative solutions for prospective and existing customers, which involves customizing, integrating and potentially selecting new tools to bring together clients’ existing technology systems and those used by the LSP.

Many client solutions experts get their start in engineering and are well versed in CAT tools, but also work to develop strong client relationship skills throughout their careers. Often professionals in this space work on the client side for at least a few years, building inside knowledge from the buyer perspective.

At the top of the tree, global managers for solutions teams build some of the most advanced workflows in commercial localization.

Career Entry Point: Localization Engineer, CAT Tools Specialist, Project Manager, Account Manager

Salary range: $130,000 – $150,000 + bonus

Key skill: ability to think creatively to create unique technology-based workflow solutions

* * *

Adaptive Globalization recruits within the translation, localization and language technology sectors from entry-level to VP+.

We love to chat with translation industry professionals about how they can make the right career moves to achieve their goals. Drop me a line at ray.green@adaptiveglobalization.com

You can check out Adaptive Globalization’s full list of vacancies for PMs, Account Managers, Loc Engineers, BDMs and more in our job listings here

Reference: http://bit.ly/2LAenLc

Creative Destruction in the Localization Industry

Creative Destruction in the Localization Industry

Excerpts from an article with the same title, written by Ameesh Randeri in Multilingual Magazine.  Ameesh Randeri is part of the localization solutions department at Autodesk and manages the vendor and linguistic quality management functions. He has over 12 years of experience in the localization industry, having worked on both the buyer and seller sides.

Te concept of creative destruction was derived from the works of Karl Marx by economist Joseph Schumpeter. Schumpeter elaborated on the concept in his 1942 book Capitalism, Socialism, and Democracy, where he described creative destruction as the “process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.

What began as a concept of economics started being used broadly across the spectrum to describe breakthrough innovation that requires invention and ingenuity — as well as breaking apart or destroying the previous order. To look for examples of creative destruction, just look around you. Artificial intelligence, machine learning and automation are creating massive efficiency gains and productivity increases, but they are also causing millions to lose jobs. Uber and other ride hailing apps worldwide are revolutionizing transport, but many traditional taxi companies are suffering.

Te process of creative destruction and innovation is accelerating over time. To understand this, we can look at the Schumpeterian (Kondratieff) waves of technological innovation. We are currently in the fifth wave of innovation ushered in by digital networks, the software industry and new media.

Te effects of the digital revolution can be felt across the spectrum. Te localization industry is no exception and is undergoing fast-paced digital disruption. There is a confluence of technology in localization tools and processes that are ushering in major changes.

The localization industry: Drawing parallels from the Industrial Revolution

All of us are familiar with the Industrial Revolution. It commenced in the second half of the 18th century and went on until the mid-19th century. As a result of the Industrial Revolution, we witnessed a transition from hand production methods to machine-based methods and factories that facilitated mass production. It ushered in innovation and urbanization. It was creative destruction at its best. Looking back at the Industrial Revolution, we see that there were inflection points, following which there were massive surges and changes in the industry.

Translation has historically been a human and manual task. A translator looks at the source text and translates it while keeping in mind grammar, style, terminology and several other factors. Te translation throughput is limited by a human’s productivity, which severely
limits the volume of translation and time required. In 1764, James Hargreaves invented the spinning jenny, a machine that enabled an individual to produce multiple spools of
threads simultaneously. Inventor Samuel Compton innovated further and came up with the spinning mule, further improving the process. Next was the mechanization of cloth weaving through the power loom, invented by Edmund Cartwright. These innovators and their inventions completely transformed the textile industry.

For the localization industry, a similar innovation is machine translation (MT). Tough research into MT had been going on for many years, it went mainstream post-2005. Rule-based and statistical MT engines were created, which resulted in drastic productivity increases. However, the quality was nowhere near what a human could produce and hence the MT engines became a supplemental technology, aiding humans and helping them increase productivity.

There was a 30%-60% productivity gain based on the language and engine that was used. There was fear that translators’ roles would diminish. But rather than diminish, their role evolved into post-editing.

The real breakthrough came in 2016 when Google and Microsoft went public with their neural machine translation (NMT) engines. Te quality produced by NMT is not yet flawless, but it seems to be very close to human translation. It can also reproduce some of the finer
nuances of writing style and creativity that were lacking in the rule-based and statistical machine translation engines. NMT is a big step forward in reducing the human footprint in the translation process. It is without a doubt an inflection point and while not perfect yet, it
has the same disruptive potential as the spinning jenny and the power loom. Sharp productivity increases, lower prices and since a machine is behind it, the volumes that can be managed are endless. And hence it renews concerns about whether translators will be needed. It is to the translation industry what the spinning jenny was to textiles, where several manual workers were
replaced by machines.

What history teaches us though is that although there is a loss of jobs based on the existing task or technology, there are newer ones created to support the newer task or technology.

In the steel industry, two inventors charted a new course: Abraham Darby, who created a cheaper, easier method to produce cast iron, using a coke-fueled furnace and Henry Bessemer, who invented the Bessemer process, the first inexpensive process for mass-producing steel. The Bessemer process revolutionized steel manufacturing by decreasing its cost, from £40 per long ton to £6–7 per long ton. Besides the
reduction in cost, there were major increases in speed and the need for labor decreased sharply.

The localization industry is seeing the creation of its own Bessemer process, called continuous localization. Simply explained, it is a fully-connected and automated process where the content creators and developers create source material that is passed for translation in continuous, small chunks. The translated content is continually merged back, facilitating continuous deployment and release. It is an extension of the agile approach and it can be demonstrated with the example of mobile applications where latest updates are continually pushed through to our phones in multiple languages. To facilitate continuous localization, vendor platforms or computer-assisted translation (CAT) tools need to be able to connect to client systems or clients need to provide CAT tool-like interfaces for vendors and their resources to use. The process would flow seamlessly from the developer or content creator creating content to the post-editor doing edits to the machine translated content. The Bessemer process in the steel industry paved the way for large-scale continuous and efficient steel production. Similarly, continuous localization has the potential to pave the way for large-scale continuous and efficient localization enabling companies to localize more, into more languages at lower prices.

There were many other disruptive technologies and processes that led to the Industrial Revolution. For the localization industry as well, there are several other tools and process improvements in play.

Audiovisual localization and interpretation: This is a theme that began evolving in recent years. Players like Microsoft-Skype and Google have made improvements in the text-to-speech, speech-to-text arena. The text to speech has become more human-like though it isn’t there yet. Speech-to-text has improved significantly as well, with the output quality going up and errors reducing. Interpretation is the other area where we see automated solutions springing up. Google’s new headphones are one example of automated interpretation solutions.

Automated terminology extraction: This is one that hasn’t garnered as much attention and focus. While there is consensus that terminology is an important aspect of localization quality, it always seems to be relegated to a lower tier from a technological advancement standpoint. There are several interesting commercial as well as open source solutions that have greatly improved terminology extraction and reduced the false positives. This area could potentially be served by artificial intelligence and machine learning solutions in the future.

Automated quality assurance (QA) checks: QA checks can be categorized into two main areas – functional and linguistic. In terms of functional QA, automations have been around for several years and have vastly improved over time. There is already exploration on applying machine learning and artificial intelligence to functional automations to predict bugs, to create scripts that are self-healing and so on. Linguistic QA on the other hand has seen some automation primarily in the areas of spelling and terminology checks. However, the automation is limited in what it can achieve and does not replace the need for human checks or audits. This is an area that could benefit hugely from artificial intelligence and machine learning.

Local language support using chatbots: Chatbots are fast becoming the first level of customer support for most companies. Most chatbots are still in English. However, we are starting to see chatbots in local languages powered by machine translation engines in the background thus enabling local language support for international customers.

Data (big or small): While data is not a tool, technology or process by itself, it is important to call it out. Data is central to a lot of the technologies and processes mentioned above. Without a good corpus, there is no machine translation. For automated terminology extraction and automated QA checks, the challenge is to have a big enough corpus of data making it possible to train the machine. In addition, metadata becomes critical. Today metadata is important to provide translators with additional contextual information, to ensure higher quality output. In future, metadata will provide the same information to machines – to a machine translation system, to an automated QA check and so on. This highlights the importance of data!

The evolution in localization is nothing but the forces of creative destruction. Each new process/technology is destructing an old way of operating and creating a new way forward. It also means that old jobs are being made redundant while new ones are being created.

How far is this future? Well, the entire process is extremely resource and technology intensive. Many companies will require a lot of time to adopt these practices. This provides the perfect opportunity for sellers to spruce up their offering and provide an automated digital localization solution. Companies with access to abundant resources or funding should be able to achieve this sooner. This is also why a pan-industry open source platform may accelerate this transformation.

The Language Industry According to LinkedIn

The Language Industry According to LinkedIn

Professional networking site LinkedIn has continued to grow since it was acquired by Microsoft for a whopping USD 26.2bn in late 2016. The site now has more than 500 million users and reportedly generated USD 1.3bn in revenues in the first quarter of 2018.

While many people continue to see LinkedIn as an online version of their resume, an increasing number of professionals find the site useful for personal branding, sales, business development, and research. Different from other social media sites like Facebook and Twitter, LinkedIn generates much of its revenue not from ad sales but from subscription services for recruiters and business development professionals. Paid subscribers are able to search LinkedIn’s extensive database in much more granular detail, which is useful for targeting potential recruits or prospective clients.

Some premium subscriptions such as Sales Navigator enable searches based on industry categories. One of the 147 such industry categories featured on LinkedIn is Translation and Localization. While not among the top categories – that honor goes to IT and Services(15m profiles), Financial Services (8.5m) and Computer Software (7.6m), the Translation and Localization category still lists an impressive 603,700 professional profiles and 21,400 so called “accounts”, i.e. LinkedIn company pages.

For what it’s worth, we sliced and diced that data and compiled a list of the top 50 countries by professional profiles and top 50 countries by company pages.

LinkedIn: Top 50 Countries in “Translation and Localization” (Personal)

Total number of personal LinkedIn profiles per country as of May 2, 2018 (top 50 countries) under industry category “Translation and Localization”

On a continental scale, Europe takes a clear lead over both North America and Asia. To the 11 translators apparently typing away in Antarctica, we salute you.

Language Industry on LinkedIn by Continent

Company pages and professional profiles in the “Translation & Localization” category by continent

Finally, let’s look at a selection of leading language industry providers and their following on the social network. Just as in real life (i.e. in terms of revenue), Lionbridge and TransPerfect battle it out for number of profiles and followers. Employees at SDL, meanwhile, seem to be more present on LinkedIn in general since, despite the relatively lower number of staff in the real world, SDL beats both TransPerfect and Lionbridge when it comes to LinkedIn profiles.

LinkedIn Presence of Large Language Service Providers

Profiles and Followers of 10 large language service providers

Of course, data from LinkedIn does not present a fully accurate picture of the size and distribution of the language industry in the real world. In Germany, to name just one example, LinkedIn struggles to gain a dominant position, competing with local alternatives such as Xing. Furthermore, translation and localization professionals working internally at large corporations may not choose Translation and Localization as their category but rather their employer’s industry.

That said, crunching LinkedIn’s Translation and Localization numbers is still interesting since it enables you to get a feel for just how big and widely-distributed this industry is.

Reference: https://bit.ly/2JP7iCa