# Demonstration of a Neural Machine Translation System with Online Learning for Translators

## TL;DR

This paper demonstrates a neural machine translation system with online learning that adapts in real-time to translator corrections, aiming to reduce post-editing effort in professional translation workflows.

## Contribution

It introduces an end-to-end platform integrating online learning into neural machine translation within a popular translation interface, enabling continuous adaptation to user corrections.

## Key findings

- System successfully integrates online learning with SDL Trados Studio.
- Continuous learning reduces post-editing effort over time.
- The platform adapts to specific domains and user styles.

## Abstract

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.