Welcome to the June ‘20 product newsletter highlights. We’ve had a busy spring, and we're excited to show you what we’ve been working on.
In recent months, we’ve focused on a number of feature updates that not only improve our customer experience, but also expand our localization capabilities. Read on to learn more.
We’re excited to announce that we now provide translation services for virtually any language! While we previously only offered translation services for 45 languages supported by our MT engine, Lilt has been updated to support translation of any language based exclusively on translation memory (TM) suggestions and human translation.
This newest capability allows Lilt to fully service the localization needs of our customers, regardless of language or geography. The world is your oyster!
For more information, please check out TM Only in our knowledge base.
A series of feature enhancements enable our TM Editor to help our customers achieve the highest localization quality:
Terminology and TM Workflow Management: Terminology changes (previously imported by users into Lilt) can now be made directly within the Lilt system.
Reviewer Terminology Approval: Reviewers are also now notified of new terminology entries when reviewing a document and can approve all proposed terms.
For more information, please check out TM Editor in our knowledge base.
Assignment Availability Notifications
We know how time consuming email can be, which is why we’re excited to announce Assignment Availability Notifications. When an assignment matching a translator’s skill set arises, Lilt will now send that translator an actionable email.
For more information, please check out Workflows for Translators in our knowledge base.
Rebuild MT models on-demand
Quality control is more important than ever, and high-quality MT is an important part of that control. If a language pair MT has been trained on bad data or is performing poorly, Lilt users can now easily reset a memory or MT to improve quality of suggestions made and accuracy of translated content.
Streamlined Tag Editing
We always aim to improve workflow efficiency, including through streamlined tag editing. A refreshed tag editor design now optimizes translator ability to quickly identify and place tag pairs around whitespace.
New visual features in Review mode now improve navigability for color blind translators. Segment states are now displayed with icons to the left of each segment, rather than with background colors.
For more information, please check out Segment State Indicators in our knowledge base.
Terminology for Reviewers
Terminology that is missing in the target will now be highlighted in the source, alerting translators and reviewers of term matches. Users can click on a highlighted source term to search it in the terminology sidebar and view all allowed translations. A "Missing Terms" tooltip will now also be displayed to the right of a segment if there are missing terms in the target.
For more information, please check out Terminology in our knowledge base.
Neural Machine Translation
Previous translation models often had issues with title-cased text, or text in all caps, such as “This” versus “this”. With our newly trained models, Lilt MT can much more accurately interpret and identify such associations, resulting in reduced translation errors and a quicker translation review process.
In addition, we built out next-gen machine translation models for a number of new languages, including:
Arabic>English, Russian>English, Farsi>English, Korean>English, Pashto>English, English<>Japanese, English<>Chinese (simplified), and English<>Japanese
MT support for these models will help our translators localize content even faster and more accurately.
Lastly, we’re excited to share that we’ve recently developed a number of new Lilt Connectors that make integrating Lilt into your current systems and workflow easier than ever:
Translation Tool Connectors
Eager for more? Sign up for our monthly newsletter here to stay up to date on our newest product developments. Thank you for reading, and thank you for translating with Lilt.
The Lilt Team