Can Neural Machine Translation be Improved with User Feedback?
This work addresses the challenge of integrating human feedback into NMT systems for practical applications like e-commerce, though it is incremental as it builds on prior simulation-based methods.
The paper tackled the problem of improving neural machine translation (NMT) using real user feedback, finding that explicit ratings were unreliable but implicit task-based feedback from an e-commerce platform successfully enhanced translation quality metrics.
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.