@inproceedings{domingo-etal-2019-demonstration,
title = "Demonstration of a Neural Machine Translation System with Online Learning for Translators",
author = "Domingo, Miguel and
Garc{\'\i}a-Mart{\'\i}nez, Mercedes and
Estela Pastor, Amando and
Bi{\'e}, Laurent and
Helle, Alexander and
Peris, {\'A}lvaro and
Casacuberta, Francisco and
Herranz P{\'e}rez, Manuel",
editor = "Costa-juss{\`a}, Marta R. and
Alfonseca, Enrique",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-3012",
doi = "10.18653/v1/P19-3012",
pages = "70--74",
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.",
}
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%0 Conference Proceedings
%T Demonstration of a Neural Machine Translation System with Online Learning for Translators
%A Domingo, Miguel
%A García-Martínez, Mercedes
%A Estela Pastor, Amando
%A Bié, Laurent
%A Helle, Alexander
%A Peris, Álvaro
%A Casacuberta, Francisco
%A Herranz Pérez, Manuel
%Y Costa-jussà, Marta R.
%Y Alfonseca, Enrique
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F domingo-etal-2019-demonstration
%X 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.
%R 10.18653/v1/P19-3012
%U https://aclanthology.org/P19-3012
%U https://doi.org/10.18653/v1/P19-3012
%P 70-74
Markdown (Informal)
[Demonstration of a Neural Machine Translation System with Online Learning for Translators](https://aclanthology.org/P19-3012) (Domingo et al., ACL 2019)
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.