Enterprises in the localization sector handle diverse content types, requiring precise localization solutions. Options range from raw machine translation to transcreation. But how can they ensure the best match between content and localization method? Traditionally, the decision relied mostly on human judgment. The PREDICT Methodology, crafted by Booking.com’s localization central team, offers a systematic framework for assessing MT suitability, aligning content type with the optimal localization solution. By integrating risk tolerance weights into binary queries about a source content and use case, PREDICT provides a score and recommended solution, from raw MT to human-only translation. This approach enables our business to provide the right quality for that specific content type, boost translation efficiency and reduce costs. Looking ahead, the methodology envisions integrating LLMs for automation and guidance, utilizing prompts to identify risk-mitigating strategies.

This session is designed to help companies and people in the business of translation evaluate MT output and to show how human translator feedback can be tweaked to make the process more objective and accurate. You will hear recommendations, insights, and takeaways on how to improve the procedure for human evaluation. When this is achieved, we can understand if the human eval study and machine metric result coheres. And we can think about what the future of translators looks like – the final “human touch” and automated MT review.”