This series of posts will take a look at some basic localization terms and concepts. These are the things you really want to know before you start localizing - key methods, top tools, and more. Not only will you know the process better, but you'll be able to make an impression on your next meeting with your localization agency. Let's dive in! In this first post of the series, we will be focusing on CAT Tools. Are you a CAT person? Ah, CAT tools. Such a cute name for such a multifunctional robust piece of software. sigh CAT stands for Computer-Aided Translation. As their name indicates, these are offline and online software tools that aid translators in their day-to-day work. They've been around for a while, and the 90s aesthetic of some of the older tools can give you serious retro nostalgia. 90s aesthetic in Trados. Source ‍ Originally, these tools were used to make translation faster by displaying the source and target strings side by side. This way, translators didn't have to go back and fourth between two files or keep looking down at the printed version. Over the years, these tools got more sophisticated. As technology developed, companies started adding in complex features like translation memories, glossaries, quality alerts, comments, and more. The massive improvements these tools introduced - in terms of time and cost management - led translation agencies to start demanding their linguists work with a CAT tool of their choice. Some tools required linguists to invest in a paid version themselves, while others allowed the agency to provide a linguist with a temporary license. Either way, the skill and added investment required when working with a CAT tool further helped agencies separate those gig-type linguists from professionals who were in it for the long run. This has been going on for a while, but over the past 5 years or so, the CAT tool market has taken a massive leap. A series of intra-industry mergers led to localization giants putting a lot of weight behind their tools. At the same time, several smaller startups launched their own CAT tool versions built for agility and productivity. This created an interesting "David and Goliath" dynamic - which I personally love to see pan out. Should you use a CAT tool? As someone who's been in the industry for a while, the short answer is YES, absolutely. If you currently have localization needs, use a CAT tool. These come in different shapes and levels of commitment, so finding the right one to fit the maturity level of your localization process is probably the biggest challenge (more on that later on). A CAT tool is a great way to navigate your project and not get lost in the details. It helps keep track of which strings are already translated and which are still pending. And the time and money saved from not having to translate or load in repetitive tasks is truly invaluable. In the long run, you'll find it'll save hours of work (and countless hairs pulled in frustration). But that's not all. CAT tools can be used to ensure your translated copy is always consistent with your terminology and with previous versions. And the built-in QA features are ideal for finding those pesky little issues the human eye skips over. Millions of mistranslations and punctuation were saved by these little CAT tool flags or alert marks. And that's before we even get into machine translation - as many of these tools can easily integrate with MT engine and let you translate thousands of words at the click of a button. Quick warning, though - don't jump into the machine translation rabbit hole unless you know exactly what you're doing. It could have dire consequences. That's how CAT looks at Weglot Not all CAT tools were born equal While it's tempting to Google 'CAT tools' and just go with the first choice on the list, that would be a mistake. Before you start browsing, sit down and think about your requirements. By knowing what you need for your localization workflow, you can match a tool to your specific needs and find the best fit for your budget, too. Some CAT tools shine where others fail, and vice versa - so it's crucial to sit and write down what's your top priorities. Is fluency the most important thing for you? Is consistency your #1 goal? Or is integration with your repository/web builder first and foremost on your list? Once you have those priorities listed, you can start running through the available options to find the ideal ones for you. For a quick overview of useful CAT tool features, you can read this post. It also compares some of the CAT tools currently available to help you find the ones offering your top preferred features. Online vs. offline CAT tools Some CAT tools - mostly the older ones - are actual desktop software you install on your computer. Others are cloud-based, which means you only need an internet connection to use them anywhere. These are advantages to each of these methods, so you'll need to consider what your workflow looks like. On the one hand, translating on the cloud is generally more convenient than installing a CAT tool on your computer. You can access the software from anywhere in the world, and you never have to worry about synchronizing data or having the most recent file saved. In addition, most of the online translators offer collaborative work tools, which allow you to work with your translators in real-time. You can leave comments and discuss translation choices with your linguist as they work, thus preventing rolling mistakes that may cost time and money. When using an online CAT tool, you can work with any linguist you choose - and they don't have to buy or set up any additional software on their machine (as long as they have a computer). This also mean you're not limited to a specific OS or have to worry about your translators having a strong enough computer. Some CAT tools even have an app or a mobile interface, letting your translators do their work or implement fixed on-the-go. This way, you'll have to wait far less time for changes or small projects to be handled. On the other hand, an online CAT tool requires a powerful internet connection - especially with added features like in-context editors. When working with remote or third-country languages, you may not be able to count on your linguists having a stable connection - and forcing them to work with cloud-based software may lead to delays. The cost model for most cloud-based CAT tools is often SaaS - forcing companies to pay a monthly fee to keep using the tools. Many of the offline tools, on the other hand, are still sold with a traditional license-based model. You pay per software license, and only have to pay again to upgrade to the latest version. Plus, as with all cloud-based SaaS tools, online CAT tools present a certain security risk. If your tool provider isn't fully on-top of security, this could present a vulnerability that may lead to data leaks or other crucial breaches. The same applies or if you don't have the right infrastructure in place to protect cloud-based integrations. How much does it cost to use a CAT tool? As mentioned, offline CAT tools are often priced per license. This means you only pay once, and only have to pay again to upgrade your license. Usually, upgrade costs are significantly lower than the original license cost. To give you an idea of the costs for using offline tools: The latest edition of Trados Studio starts at $2800. Note that to use Trados, your linguists will have to buy their own license for the software, which may limit your linguist pool. Other offline tools, like MemoQ, will charge you $175 per month - but you'll get the added advantage of 'assigning' each linguist a license when they need to use the software. The cost for cloud-based tools, on the other hand, varies significantly. Weglot will charge you from $9.90 to $199 for their product, and Localazy offers similar pricing - from $9 to $199. Phrase starts at $23 and goes higher for the advanced and enterprise editions. Transifex will charge you starting at $70 a month, and CrowdIn starts at $49 and goes up to $1,500. Note that some of these tools limit the amount of strings or words you can translate with them on certain tiers - so it's a good idea to consider the workload you're looking at before making a decision. That’s it for the first post in this new series. I look forward to bringing you more basic localization concepts in the coming weeks and months. Feel free to leave a comment about your experiences with localization tools! I'd love to hear more and help with any questions. ‍

As the role of human translators continues to shrink, it is up to us to redefine our value in the industry. When I was a kid, back in the olden days, we had a family tradition. Every once in a while, we’d all go together to the video rental store, argue a lot, pick out a film and watch it together. These video stores were truly magical places — aisles and aisles of every cartoon I could imagine (though we didn’t have imdb back then, so I couldn’t imagine much). When we finished the movie, we rewound the tape (we were well-mannered kids) and took it back in 48 hours or less. By the time I was in high school, most of the video stores shut down. Instead, we drove over to the vending machine at the edge of town, pressed a few buttons and were rewarded with a thin plastic DVD box sliding out. These days, even vending machines are getting more and more scarce. With Netflix and Hulu on every computer and smart TV, an entire profession has just — poof — disappeared. Sounds familiar? Being a translator, this is something I hear around me constantly. There are powerful winds of change currently flowing through the translation world, brought on by the rise of AI usage in machine translation. Prophecies of doom proclaim that translation is a dying profession, and translators a breed on the verge of extinction. While I can’t say I agree with the doomsday approach, things are definitely going to change. In fact, over the past couple of years, the entire industry is swept up in a sense of upcoming transformation: a feeling I personally find at the same time both terrifying and exhilarating. For many of my colleagues, these are scary times. Translators have seen a steady rise in demand for their services over the past 20 years. In 2006, the US Department of Labor predicted a 24% increase in request for translators over the following 10 years. Thanks to globalization and UX trends, more and more companies dove into more and more markets, localizing their products as a result. If you were lucky enough to have a knack for languages and the right training, you were assured a rather safe job prospect in a not-so-safe market. But that cozy status-quo started to crumble in 2015 when the first neural machine translation system went live. With machine translation — in one form or other — being available for decades, no one could estimate how transformative this was going to be. But NMT and the forms of AI-based machine translation that followed brought such rapid improvement in only a few years, it quickly became clear that the tables are about to be turned. AI relies on big data, and while some languages and specialties already present better results, all of them will get there sooner or later — sooner being the key word. As the role of human translators in good-old source-to-target translation continues to shrink, it is up to us to redefine our value and find ways to maintain our relevance in an evolving industry, lest we go the way of the video store clerk. To find out how we, as human linguists, can make a meaningful contribution in hybrid human-machine translation, we first have to consider the characteristics of AI and machine learning, and more importantly — their weaknesses. AI-based tools excel in speed, accuracy, and efficiency. In the near future, machine learning will make our lives faster, cheaper, more efficient and accurate. Like the most realistic science fiction ever made, every aspect of our world will be changed and improved, whether we like it or not: from food production, all the way through medicine, supply, planning, driving and even home living. And the contribution it will have to our life does not end with technical tasks. Based on the binary rules it is given— or the ones it develops using the data it is fed — AI can produce ‘creative’ content that is remarkably human-like. Apply a little lipstick and don’t let anyone get too close, and they will never know the difference. The output will be technically correct and the process generally a great deal quicker than a human is capable of. Have a look, for example, at the paragraph below, produced by Allen Institute’s GPT-2 Explorer: The first major change to the translation industry was the introduction of the “translator’s manual” in the early 1990s. This manual was a major step forward in the translation industry. It was a step forward in the translation industry because it allowed the translator to make a decision about the translation of a book. The translator could then make a final judgment based on that decision. The second major change was the creation of a new translation service called “translation service” (TOS). This service was created by the translation service company, Translate.com. The TOS service was a service that allowed the translator to make a decision about the translation of a book. Sure, GPT-2 may not be the brightest match in the box yet, and it seems to be weirdly fixated on book translation. However, considering the fact that this was written by a computer, I must admit it’s quite impressive. The content is grammatically correct and the flow makes sense — well, sort of. Actually, some of my papers in high-school weren’t so far off. And as GPT-2 continues to learn, it may even get to college soon. In fact, on top of translation and writing, scientists managed to use machine learning to get computers to output human-like results in many other fields. Last year, Christie’s sold an AI-generated portrait for $435,000. Tools like AIVA and Amper Music offer users custom-made AI compositions for their projects. And just a few months ago, an AI-entity named Benjamine created a film starring nonother than Baywatch’s David Hasselhoff. I know, right? If computers are smart enough to make us some Hassel-clones, humanity can finally sit back and admire a job well done. But these artistic endeavors lack one thing — a human touch. AI’s Achilles’ heel, or its weak spot, is the illogical things that make us — us. Faith, emotions, culture, empathy — all of those little, random parts of humanity that make no sense but somehow drive us forward. Without them, the world will feel a cold, strange and alienating place. While people may be able to appreciate the precision and perfection of an AI-written piece, they may not be able to connect with it on a deeper emotional level. The human touch is unquantifiable, undefinable. And since computers can’t understand it, they can’t replicate or reproduce it. To bridge that gap, many pre-AI professions will become hybrids — with computers doing the bulk of the technical work, and humans adding their own unique fingerprint to create a complete product that is larger than the sum of its parts. In the case of translators, it’s time to shift our focus from the technical act of translation to the translation endeavor as a whole. The skills linguists are required to have today will soon become obsolete. We will no longer need people to read content in one language and type it in another. Just like a robotic vacuum cleaner, computers will do that for us (hopefully, not getting stuck under the couch quite as often). Rather than being mere language experts, translators will have to become anthropologists of a sort — anthro-linguists, if you’d like — students of human nature and culture. Specifically, our role in the translation process will be twofold. First, we will act as masters of humanity: make sure the soul of the content is properly conveyed. This is something I came across recently, in a website translation job we worked on for a major client in the hospitality industry. On top of our usual MTPE duties (because let’s face it, MT is not there yet), we verified that the tone and voice match those of the brand: inviting, friendly and professional. And it’s a good thing we did! NMT and similar methods do employ a meaning-based approach, but they are prone to overlook subtle innuendos and subtext. More often than not, the meaning-based approach produced content that was correct, but also dry and impersonal. Sure, it probably would have been understood either way. But it might not have made their customers feel welcome, or delighted, and a lot of the carefully-crafted effect of the original content would have been lost. As anthro-linguists, it will be our job to adapt translations, keeping them in line with the required tone and voice. Secondly, we will act as cultural experts for the target market, ensuring that the result is well-understood, that it doesn’t step on any toes (unless the point is that it does) and that it maintains the same cultural spirit as the source. Humor; references to pop-culture, history, and books; or even simple visuals are all at risk of getting lost in translation, or accidentally offending our market. An outstanding example of how seemingly harmless content can have a detrimental effect on your brand’s image is the case of the German brewery Eichbaum. In honor of the 2018 World Cup, Eichbaum printed bottle caps with flags of the 32 national teams participating. But one of the countries — Saudi Arabia — found their marketing act extremely offensive. The Saudi flag contains the Islamic statement of faith, and the choice to print it on an alcoholic beverage — the consumption of alcohol being “haram”, a serious prohibition in Islam — incited a heavy backlash from Muslims around the world. Eichbaum clearly did not think their marketing choice will cause such outrage, but an expert in Saudi-Arabian culture could have probably tipped them off, have they taken the trouble to ask. The switching of roles in translation means that linguists interested in keeping their job in the industry will have to acquire a completely different set of skills to stay relevant. Each translation job will involve diving deep into the hidden meanings of the text, using detailed client briefs and a lot of creativity. Translators will need to have an in-depth, broad understanding of their native culture, as well as its many sub-cultures; keep themselves up to speed with any new social phenomenon; And employ various methods of research and data collection to gain insights on the markets they focus on. By using our own human abilities to complement the advantages of AI and machine learning, translators will remain valuable in today’s disturbed industry. Even more so, we can take an active role in the transformation, helping the translation industry become bigger, better and stronger. And we can be part of the revolution, at the same time making sure the content by which we, as a society, are surrounded preserves its sense of humor, its unique character, and most importantly — its soul.