DeepAI AI Chat
Log In Sign Up

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

by   Miguel Domingo, et al.
Universitat Politècnica de València

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.


page 1

page 2

page 3

page 4


Online Learning for Neural Machine Translation Post-editing

Neural machine translation has meant a revolution of the field. Neverthe...

Incremental Adaptation of NMT for Professional Post-editors: A User Study

A common use of machine translation in the industry is providing initial...

Online Learning for Effort Reduction in Interactive Neural Machine Translation

Neural machine translation systems require large amounts of training dat...

A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits

The advantages of neural machine translation (NMT) have been extensively...

Tigrinya Neural Machine Translation with Transfer Learning for Humanitarian Response

We report our experiments in building a domain-specific Tigrinya-to-Engl...

Continuous Learning in Neural Machine Translation using Bilingual Dictionaries

While recent advances in deep learning led to significant improvements i...

SYSTRAN's Pure Neural Machine Translation Systems

Since the first online demonstration of Neural Machine Translation (NMT)...