In the past, translation was measured solely on process-based methods and manual evaluations, where success hinged on the meticulousness of your process and trust of your evaluators.
Fast forward to the present, where neural-based models, dedicated machine translation and new Large Language Models (LLMs), have revolutionized the landscape, making the translation process significantly smoother and less labor-intensive while improving quality in many cases.
What does the future hold? In this discussion, we dig into the realm of granular segment analysis, a new approach that will unlock the potential to concentrate effort and resources only where it is needed.
Gain insights into the past, present, and future of translation processes, and discover how technology is reshaping the way we approach language translation.
This conversation explores:
- How AI is reshaping the way we approach the translation process
- The nuances of efficiency and quality
- Ways to implement AI-powered processes to stay ahead of the curve
