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arXiv · 1706.03196

Online Learning for Neural Machine Translation Post-editing

Álvaro Peris·Luis Cebrián·Francisco Casacuberta
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Abstract

Neural machine translation has meant a revolution of the field. Nevertheless, post-editing the outputs of the system is mandatory for tasks requiring high translation quality. Post-editing offers a unique opportunity for improving neural machine translation systems, using online learning techniques and treating the post-edited translations as new, fresh training data. We review classical learning methods and propose a new optimization algorithm. We thoroughly compare online learning algorithms in a post-editing scenario. Results show significant improvements in translation quality and effort reduction.

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Álvaro Peris, Luis Cebrián, Francisco Casacuberta. 2017-06-10. Online Learning for Neural Machine Translation Post-editing. https://arxiv.org/abs/1706.03196

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