k-nearest-neighbor machine translation (NN-MT), proposed by Khandelwal et al. (2021), has achieved many state-of-the-art results in machine translation tasks. Although effective, NN-MT requires conducting NN searches through the large datastore for each decoding step during inference, prohibitively increasing the decoding cost and thus leading to the difficulty for the deployment in real-world applications. In this paper, we propose to move the time-consuming NN search forward to the preprocessing phase, and then introduce Nearest Neighbor Knowledge Distillation (NN-KD) that trains the base NMT model to directly learn the knowledge of NN. Distilling knowledge retrieved by NN can encourage the NMT model to take more reasonable target tokens into consideration, thus addressing the overcorrection problem. Extensive experimental results show that, the proposed method achieves consistent improvement over the state-of-the-art baselines including NN-MT, while maintaining the same training and decoding speed as the standard NMT model.

本文提出一种改进机器翻译模型的方法，即将 NN search 前置，并通过最近邻知识蒸馏（NN-KD）训练基本 NMT 模型直接学习NN知识，可以更好地解决机器翻译中的过度纠正问题，并在保持训练和解码速度不变的情况下，实现了比NN-MT等现有方法更好的结果。

神经机器翻译最近邻知识蒸馏