Can Neural Machine Translation be Improved with User Feedback?

ACL 2018

Can Neural Machine Translation be Improved with User Feedback?

Jan 21, 2021
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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. Authors: Julia Kreutzer, Shahram Khadivi, Evgeny Matusov, Stefan Riezler (Heidelberg University)

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