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We study the problem of online learning with human feedback in thehuman-in-the-loop machine translation, in which the human translators revisethe machine-generated translations and then the corrected translations are usedto improve the neural machine translation (NMT) system. However, previousmethods require online model updating or additional translation memory networksto achieve high-quality performance, making them inflexible and inefficient inpractice. In this paper, we propose a novel non-parametric online learningmethod without changing the model structure. This approach introduces twok-nearest-neighbor (knn) modules: one module memorizes the human feedback,which is the correct sentences provided by human translators, while the otherbalances the usage of the history human feedback and original NMT modelsadaptively. Experiments conducted on EMEA and JRC-Acquis benchmarks demonstratethat our proposed method obtains substantial improvements on translationaccuracy and achieves better adaptation performance with less repeating humancorrection operations.

Non-Parametric Online Learning from Human Feedback for Neural Machine Translation