Title: Can Neural Machine Translation be Improved with User Feedback?,
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.