  We study the problem of online learning with human feedback in the
human-in-the-loop machine translation, in which the human translators revise
the machine-generated translations and then the corrected translations are used
to improve the neural machine translation (NMT) system. However, previous
methods require online model updating or additional translation memory networks
to achieve high-quality performance, making them inflexible and inefficient in
practice. In this paper, we propose a novel non-parametric online learning
method without changing the model structure. This approach introduces two
k-nearest-neighbor (knn) modules: one module memorizes the human feedback,
which is the correct sentences provided by human translators, while the other
balances the usage of the history human feedback and original NMT models
adaptively. Experiments conducted on EMEA and JRC-Acquis benchmarks demonstrate
that our proposed method obtains substantial improvements on translation
accuracy and achieves better adaptation performance with less repeating human
correction operations.
