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

arXiv: https://arxiv.org/abs/1906.09000 · HTML: https://mlsanity.com/paper/1906.09000/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Non-Parametric Online Learning from Human Feedback for Neural Machine Translation](https://mlsanity.com/paper/2109.11136/) (2021)
- [Cost-Effective Training in Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2201.05700/) (2022)

## Abstract (arXiv)

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.