Press Release

Translated’s New MT Solution Fuses Advances in AI with Human Expertise to Deliver Real-Time Quality Improvements

  • ModernMT now offers customers the ability to fine-tune their adaptive models in real-time, through ongoing, targeted reviews of low-confidence MT translations.
  • Translations are delivered instantly via API, while revisions are performed in the background by qualified translators for daily updates to the MT engine.
  • Customers can monitor translation quality and vary the amount of human involvement to increase the pace of improvement.

ROME–(BUSINESS WIRE)–Translated, a leading provider of AI-powered language solutions, today unveiled the first dynamic, adaptive machine translation (MT) system that enables enterprises to continually improve MT quality in real-time through a unique symbiosis between humans and machines. ModernMT’s new Human-in-the-Loop (HITL) plan allows users to strategically allocate resources and scale the involvement of translators to ensure continuous improvement in MT at a faster rate than ever before. For the first time, the MT model improves daily through automated, targeted reviews that happen in the background while still delivering instant translations via the API, without sacrificing speed or scale.




Previously, MT buyers have had to rely on periodic evaluations or direct user feedback to understand where MT translations could be improved. Even with that information, updating the model is cumbersome, if even possible. The HITL plan addresses these obstacles with a ‘Machine-First, Human-Optimized’ approach. While suitable for all MT users, the new solution will be particularly effective for organizations that rely on raw MT output (e.g., in real-time and/or high-volume scenarios) and have no means of identifying potential issues in the output, or addressing them, in any regular or consistent way. This solution simplifies the process of monitoring quality and deciding where best to invest in improvements across languages and content types.

Our Human-in-the-Loop plan reimagines adaptive machine translation for enterprises. It’s designed to be dynamic, flexible, and deeply integrated with translator expertise, especially for large MT-driven projects,” – said Marco Trombetti, CEO of Translated – “This elegant solution enables organizations to monitor, plan, and invest in continuous MT quality improvement in a highly automated, cost-effective, and structured way.”

Translated’s HITL approach is a true innovation that combines the best elements of AI, highly skilled people, and efficient workflows, to allow enterprises to use MT to scale operations internationally.

Users can easily adjust what proportion of their machine translated content should be reviewed, from 0% (no review) upwards, in a new, easy-to-use interface. This can be done for any language combination and the user gets an instant estimate of cost variations. The system automatically selects which translations to review using a new AI-powered quality assessment feature that ultimately adapts to the users’ content to identify the most appropriate segments for review.

Reviews are carried out by professional translators selected from Translated’s community of over 300,000 vetted, native-speaking professionals. The most suitable translators for a given task are chosen by a proprietary, AI-powered tool that considers over 30 parameters, including past performance and availability. The MT engine is subsequently re-trained continuously with corrections it receives.

The new Human-in-the-Loop plan will be available to new and existing Translated customers directly at www.modernmt.com and through their account managers beginning December 19, 2023. Existing customers will have the option to upgrade to the new Human-in-the-Loop plan within the new ModernMT interface at any time.

Translated is hosting a live event for those interested in a deeper understanding of this innovation and how it can help them grow their business.

Contacts

Silvio Gulizia
Head of Content

Mail: silvio@translated.com
Mob.: +39 393 1044785

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