Learn, discover, and explore everything you need to know about localization
Welcome to the August 2019 Product Newsletter. Languages were a big focus for Lilt this month, with support for two new languages, the first non-English language pairs, support for regional differences within languages, and much more.
Welcome to the July 2019 Product Newsletter. We've been busy this summer building new ways to get content into Lilt, supporting new languages, and even the ability to machine translate entire documents.
Lilt is happy to announce the addition of Bulgarian and Slovenian to our platform today. With this, Lilt officially supports 40 languages; or 50 including variations of languages such as Castilian and Latin American Spanish.
The stress of taking a single brick from the tower and placing it at the top; the mounting intensity with each move made: Anyone who’s ever played a game of Jenga understands this feeling. Imagine playing Jenga with a huge tower in front of a live audience, where any misstep means total disaster. My team’s experience migrating an application from AngularJS to React has felt like a similar challenge.
Computer science terms once exclusively used in scientific communities have become ubiquitously integrated into our daily lives—the news we read, products we consume, and the technology we use. Some are used interchangeably with one another (incorrectly), others are hazy in definition and application. In light of this, we thought we’d lend clarity to the top Machine Translation (MT) related computer science terms you may encounter.
Translation is a complex art form – one we highly respect. The manual process involved makes it difficult to keep up with changing technology and progressive business practices. Modernizing seems formidable. Change breeds uncertainty. It’s understandable for localization teams to stick with legacy vendors, a static stack, and the same workflows.
We’re pleased to announce a new feature in our interface that improves the translation review process. Previously, reviewers could make general text comments about errors and introduce categories by using hashtags to indicate the type of error (such as #punctuation or #mistranslation). However, it was still a manual process to collect the different hashtags and identify precisely where the error occurred.
There’s little we love more than helping our customers succeed, and providing real value in their localization efforts.
We had the pleasure of joining the SF Globalization meetup this past Wednesday, Feb. 13, at the Autodesk Gallery in San Francisco, where our CEO Spence Green gave a presentation on recent advancements in neural machine translation.
Creating an ROI model for localization is a pain point for many organizations. We’re dedicated to helping solve this problem and have written a complete guide on the very issue. You can get your free copy here. First, we wanted to spend some time dissecting the complications of localization and ROI.