Demonstration of a Neural Machine Translation System with Online Learning for Translators
This is an incremental improvement for professional translators using SDL Trados Studio to save effort in post-editing tasks.
The paper tackled the problem of reducing post-editing effort in neural machine translation by introducing a system that implements online learning from translator corrections in a production environment, integrated with SDL Trados Studio.
We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment. These techniques allow the system to continuously learn from the corrections provided by the translators. We implemented an end-to-end platform integrating our machine translation servers to one of the most common user interfaces for professional translators: SDL Trados Studio. Our objective was to save post-editing effort as the machine is continuously learning from human choices and adapting the models to a specific domain or user style.