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://preview.aclanthology.org/build-pipeline-with-new-library/P19-3012/
- DOI:
- 10.18653/v1/P19-3012
- 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)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/P19-3012.pdf
- Code
- midobal/OpenNMT-py