@InProceedings{kreutzer-EtAl:2018:N18-3,
  author    = {Kreutzer, Julia  and  Khadivi, Shahram  and  Matusov, Evgeny  and  Riezler, Stefan},
  title     = {Can Neural Machine Translation be Improved with User Feedback?},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans - Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {92--105},
  abstract  = {We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform. Previous work has been confined to simulation experiments, whereas in this paper we work with real logged feedback for offline bandit learning of NMT parameters. We conduct a thorough analysis of the available explicit user judgments---five-star ratings of translation quality---and show that they are not reliable enough to yield significant improvements in bandit learning. In contrast, we successfully utilize implicit task-based feedback collected in a cross-lingual search task to improve task-specific and machine translation quality metrics.},
  url       = {http://www.aclweb.org/anthology/N18-3012}
}