Manuel Herranz Pérez
2019
Demonstration of a Neural Machine Translation System with Online Learning for Translators
Miguel Domingo
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Mercedes García-Martínez
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Amando Estela Pastor
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Laurent Bié
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Alexander Helle
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Álvaro Peris
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Francisco Casacuberta
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Manuel Herranz Pérez
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
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.