  kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines
pre-trained neural machine translation (NMT) model with token-level
k-nearest-neighbor (kNN) retrieval to improve the translation accuracy.
However, the traditional kNN algorithm used in kNN-MT simply retrieves a same
number of nearest neighbors for each target token, which may cause prediction
errors when the retrieved neighbors include noises. In this paper, we propose
Adaptive kNN-MT to dynamically determine the number of k for each target token.
We achieve this by introducing a light-weight Meta-k Network, which can be
efficiently trained with only a few training samples. On four benchmark machine
translation datasets, we demonstrate that the proposed method is able to
effectively filter out the noises in retrieval results and significantly
outperforms the vanilla kNN-MT model. Even more noteworthy is that the Meta-k
Network learned on one domain could be directly applied to other domains and
obtain consistent improvements, illustrating the generality of our method. Our
implementation is open-sourced at https://github.com/zhengxxn/adaptive-knn-mt.
