Improving machine translation (MT) by learning from human post-edits is a powerful solution that is still unexplored in the neural machine translation (NMT) framework. Also in this scenario, effective techniques for the continuous tuning of an existing model to a stream of manual corrections would have several advantages over current batch methods. First, they would make it possible to adapt systems at run time to new users/domains; second, this would happen at a lower computational cost compared to NMT retraining from scratch or in batch mode.To attack the problem, we explore several online learning strategies to stepwise fine-tune an existing model to the incoming post-edits. Our evaluation on data from two language pairs and different target domains shows significant improvements over the use of static models.

Continuous Learning from Human Post-Edits for Neural Machine Translation

Marco, Turchi;Matteo, Negri;Farajian, M.;Marcello, Federico
2017-01-01

Abstract

Improving machine translation (MT) by learning from human post-edits is a powerful solution that is still unexplored in the neural machine translation (NMT) framework. Also in this scenario, effective techniques for the continuous tuning of an existing model to a stream of manual corrections would have several advantages over current batch methods. First, they would make it possible to adapt systems at run time to new users/domains; second, this would happen at a lower computational cost compared to NMT retraining from scratch or in batch mode.To attack the problem, we explore several online learning strategies to stepwise fine-tune an existing model to the incoming post-edits. Our evaluation on data from two language pairs and different target domains shows significant improvements over the use of static models.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/313135
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