Automatic Post-Editing

As a first step, the auto-adaptive neural machine translation model provides accurate and efficient translations at scale.

It continuously learns from external inputs, such as translation memory data, bilingual dictionaries, and real-time post-editing feedback, refining its output over time through a built-in feedback loop.

Next, the machine translation quality estimation (MTQE) model leverages expert-labelled and annotated data to automatically assess each translated sentence.  

The model replicates our own language specialist's decision-making by categorizing translation as good, adequate, or poor. Flagging, lower-quality translations directly improves content efficiently.   

Following MTQE, a private large language model (LLM) performs automatic post-editing of segments that require further refinement. This generative AI model enhances the accuracy and fluency of translations, reducing the need for human intervention. 

By automating this crucial step, it delivers higher-quality translations faster and more efficiently, contributing to significant cost savings and improved time to market. 

Our NMT models adapt and learn continuously through a built-in feedback loop, incorporating quality assessment outcomes, and generative AI output.  

This adaptive learning process ensures that translations become increasingly accurate and refined over time, meeting your increasing translation requirements.