The Key to Mastering AI for Localization

By Kirti Vashee

As companies increasingly consider using AI for localization, many face a key problem: their existing multilingual data doesn't work effectively with the latest AI technologies. Traditional translation memories (TMs), which often consist of isolated segments without broader context, do not meet the requirements of the new generation of AI systems. Plus, glossaries, style guides, and other resources like corporate tone guidelines and general instructions are typically structured based on a project approach rather than a data approach, which is not helpful when using AI for translation.

This misalignment often leads to inefficiencies, inconsistencies, and errors, especially in multi-vendor environments. Traditional workflows also struggle to properly integrate and interact with modern AI systems, hindering their ability to scale and adapt to evolving localization needs.

Data curation is critical in improving the quality of AI translation output. Properly curated data ensures accurate, relevant, and contextual translations. High-quality, metadata-enabled, context-rich TMs improve AI accuracy, provide culturally appropriate translations, and support continuous learning, significantly reducing the need for corrective human intervention. When properly designed, the AI can use glossaries and style guides to fine-tune the model to better meet specialized business needs.

Organizations that wish to address these issues need to establish a comprehensive data-curation framework:

• Data cleansing: Regularly cleanse and update TMs to ensure relevance and accuracy.
• Data-source enhancement: Incorporate glossaries, style guides, and contextual information beyond the standard TMs to enrich the data available to AI.
• Collaborative refinement: Combine AI capabilities with human expertise to provide AI with insights it may be missing, continuously improving data quality and, in turn, AI performance.

Effective data curation transforms AI from a promising tool into a powerful localization technology engine, making it a valuable asset to your strategy. By overcoming the limitations of traditional TMs through data cleansing and metadata enrichment, you can unlock AI's full potential in localization.

To learn more about optimizing your localization workflow with AI, read our website's in-depth article on data curation.

P.S.: Considering an AI-first localization workflow? We can assess the AI readiness of your multilingual data and provide insights into the quality of AI translation and the impact of data curation on long-term savings. Book a call now.

Join Us at LocWorld to Explore AI-First Localization and AI Voices

Later this month, our team will be at LocWorld in Monterey (October 28–30). While there, we'll be glad to offer a preview of our upcoming AI-first localization technologies. If you're attending, feel free to book a meeting with Michael Stevens , Roberto Sastre , Chris Thomas , or Cody Carlson .

We're proud to announce that Translated has been selected as a finalist in LocWorld’s Process Innovation Challenge, showcasing our commitment to using AI for good through our Voice for Purpose project. Endorsed by the World Health Organization, this initiative provides expressive voices to individuals who have lost the ability to speak due to conditions such as ALS and laryngeal cancer.

Our Director of Audiovisual, Fabio Minazzi , will showcase our innovative platform for real-time voice delivery, integrating expressive AI voice technology that generates speech with natural prosody, rhythm, and tone—bringing lifelike voices to people who depend on synthetic voices.

With the support of over 5,000 donors, we've developed voices in five languages, benefiting people all around the world.

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