“What technologies do I need to power my localization program?” It’s a common question among global businesses just getting started with localization, but it’s a little too broad. There are so many tools that can enhance your program, but you first need to know what needs enhancing. A better place to start would be with more specific questions around the problem areas you’d like localization technology to solve. Those are the kinds of questions we’ll explore here. You’ll learn all about the basic “starter pack” of localization technologies (as we like to think of it) before we touch on the latest tech developments. First, for a little context, you should know about the mother of all localization technologies: the translation management system (TMS). This is the platform that stitches together all the tools companies use to manage their translations. It will be the core of your technology stack, and requires serious planning before you buy. (Here’s an ebook that will help guide how to choose one.) All other localization technologies you’ll hear about—some of them components of TMS—tend to fall into one of three categories: Computer-assisted translation (CAT) tools Machine translation (MT) tools Linguistic quality assurance (LQA) tools Here are examples of common problems you might be trying to solve. Even more specific questions might be: How do you help your translators reuse work that has already been done? Or how do you make sure the translator has the right context and the information they need? For this, you’ll need CAT tools, which are a central component of any TMS. Specifically, they have three functions that can help you in your efforts: Translation Memory (TM). A TM is a database of completed translations the translator can pull from so that when they are translating something identical or very similar to a past translation, they don’t have to reinvent the wheel. Terminology database. Not only might you have repeat content, but you likely use industry terminology. Most CAT tools include a terminology database that provides translations plus information around how and when to use these terms. Visual context, also known as in-context review, is available for some, but not all, file types and/or CAT deployments. When it is available, translators can see the format in which the content will be published as they work. Using one or more of these tools helps control content consistency across formats and languages. Automation can help translators work faster, but must be carefully applied if you’re going to produce content fast and to a high standard. In this case, you want to think about the components of a TMS that can automate your workflows. One option, again, is to use translation memory to pull past translations. Say you’re updating a product manual you’ve translated before: your TM should automatically recycle a large portion of that content, leaving time-consuming manual work behind. Another option, which you might apply to the remaining untranslated content after the TM has done its work, is machine translation (MT). MT engines can translate text without human intervention—think tools like Google Translate. At its current stage of advancement, MT can handle basic, informational content like knowledge-base articles, user manuals and customer chat bots. Typically, you’ll want to take a hybrid approach and combine MT with human post-editing, unless the content is of limited and temporary use. (For persuasive content that carries emotional weight, you’ll probably want to forego MT for now.) As a bonus, TM and MT don’t just make humans more efficient. They also provide inspiration. Past translations can clue translators in to better ways to translate things in terms of verbiage or client preferences. Which is better for translators to use—TM or MT? Check out this post to dive deeper. This is where the third category of localization technology comes in: linguistic quality assurance (LQA) tools. These tools flag basic errors like spelling or punctuation mistakes, inconsistent capitalization, extra spaces and terminology mismatches for the human reviewer to clean up. They speed up work and eliminate careless human errors, but humans are still needed to make the final edits, since the technology isn’t advanced enough to fully rely on yet and still generates false positives. You can also set up customized rules-based QA tools to check for certain types of phrasing that might be problematic to your target market, like geopolitical statements with mentions of contested territories. QA tools can help catch cultural mistakes that have big implications for your brand. LQA tools can also integrate with your TMS so linguists can access them from within the translation environment. A company expanding into Europe might not want to invest a lot of money in Greece in the wake of its economic crisis. But that’s not to say Greece is completely unviable. To gauge consumer interest in your product in new markets like these, you could use a combination of MT and crowdsourcing. Crowdsourcing platforms (sometimes deployed as a superset of CAT tools) tend to offer lower-cost translations, so there’s less investment at stake. You start with MT, then, through crowdsourcing platforms—accessible by “crowds” of translators who all work on small pieces of your project simultaneously—you can leverage multiple native-language speakers to suggest improvements to your machine-generated translations rather than using more expensive (though more reliable) professional post-editors. The danger is, you release sub-par and potentially inconsistent translations, but it’s not a bad move in markets where any risk of a tarnished brand footprint will be smaller. (Do not try this in a major market.) You’ve probably heard of artificial intelligence, and yes, AI is disrupting localization as much as any other industry. The above technologies will get you started, but as your localization program matures, you might find yourself asking questions that can only be answered with AI-powered solutions. For example: How can we route jobs to best-fit translators? (Crowdsourcing platforms are advancing to automate this process.) How can we track and manage the costs of many small transactions? (AI can help build powerful dashboards.) How can we perform sentiment analysis on multilingual content? (Google has developed tools like the Natural Language API to classify and analyze speech.) How can we machine-translate persuasive content? (Nothing on the horizon here. We’ll just have to wait and see if MT is eventually up to it.) In the meantime, have a think about the business problems you need localization technology to solve, and use those to guide your buying choices.