Generating multiple translation candidates would enable users to choose the
one that satisfies their needs. Although there has been work on diversified
generation, there exists room for improving the diversity mainly because the
previous methods do not address the overcorrection problem -- the model
underestimates a prediction that is largely different from the training data,
even if that prediction is likely. This paper proposes methods that generate
more diverse translations by introducing perturbed k-nearest neighbor machine
translation (kNN-MT). Our methods expand the search space of kNN-MT and help
incorporate diverse words into candidates by addressing the overcorrection
problem. Our experiments show that the proposed methods drastically improve
candidate diversity and control the degree of diversity by tuning the
perturbation's magnitude.

通过引入扰动 k 最近邻机器翻译（kNN-MT）方法，本文提出了一种生成更多样化翻译的方法，解决了过校正问题，改善了候选翻译的多样性，并通过调整扰动的幅度来控制多样性程度。

用扰动的 kNN-MT 生成多样化的翻译

Generating Diverse Translation with Perturbed kNN-MT

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。