  k-Nearest-Neighbor Machine Translation (kNN-MT) becomes an important research
direction of NMT in recent years. Its main idea is to retrieve useful key-value
pairs from an additional datastore to modify translations without updating the
NMT model. However, the underlying retrieved noisy pairs will dramatically
deteriorate the model performance. In this paper, we conduct a preliminary
study and find that this problem results from not fully exploiting the
prediction of the NMT model. To alleviate the impact of noise, we propose a
confidence-enhanced kNN-MT model with robust training. Concretely, we introduce
the NMT confidence to refine the modeling of two important components of
kNN-MT: kNN distribution and the interpolation weight. Meanwhile we inject two
types of perturbations into the retrieved pairs for robust training.
Experimental results on four benchmark datasets demonstrate that our model not
only achieves significant improvements over current kNN-MT models, but also
exhibits better robustness. Our code is available at
https://github.com/DeepLearnXMU/Robust-knn-mt.
