  Nearest neighbor machine translation is a successful approach for fast domain
adaption, which interpolates the pre-trained transformers with domain-specific
token-level k-nearest-neighbor (kNN) retrieval without retraining. Despite kNN
MT's success, searching large reference corpus and fixed interpolation between
the kNN and pre-trained model led to computational complexity and translation
quality challenges. Among other papers, Dai et al. proposed methods to obtain a
small number of reference samples dynamically for which they introduced a
distance-aware interpolation method using an equation that includes free
parameters. This paper proposes a simply trainable nearest neighbor machine
translation and carry out inference experiments on GPU. Similar to Dai et al.,
we first adaptively construct a small datastore for each input sentence.
Second, we train a single-layer network for the interpolation coefficient
between the knnMT and pre-trained result to automatically interpolate in
different domains. Experimental results on different domains show that our
proposed method either improves or sometimes maintain the translation quality
of methods in Dai et al. while being automatic. In addition, our GPU inference
results demonstrate that knnMT can be integrated into GPUs with a drop of only
5% in terms of speed.
