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.
@article{arxiv.1706.03196,
title = {Online Learning for Neural Machine Translation Post-editing},
author = {Álvaro Peris and Luis Cebrián and Francisco Casacuberta},
journal= {arXiv preprint arXiv:1706.03196},
year = {2017}
}