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

Miguel Domingo, Mercedes García-Martínez, Amando Estela Pastor, Laurent Bié, Alexander Helle, Álvaro Peris, Francisco Casacuberta, Manuel Herranz Pérez


Abstract
We present 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. We pretend to save post-editing effort as the machine is continuously learning from its mistakes and adapting the models to a specific domain or user style.
Anthology ID:
P19-3012
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Marta R. Costa-jussà, Enrique Alfonseca
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
70–74
Language:
URL:
https://aclanthology.org/P19-3012
DOI:
10.18653/v1/P19-3012
Bibkey:
Cite (ACL):
Miguel Domingo, Mercedes García-Martínez, Amando Estela Pastor, Laurent Bié, Alexander Helle, Álvaro Peris, Francisco Casacuberta, and Manuel Herranz Pérez. 2019. Demonstration of a Neural Machine Translation System with Online Learning for Translators. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 70–74, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Demonstration of a Neural Machine Translation System with Online Learning for Translators (Domingo et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-3012.pdf
Code
 midobal/OpenNMT-py