k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as
a non-parametric solution for domain adaptation in neural machine translation
(NMT). It aims to alleviate the performance degradation of advanced MT systems
in translating out-of-domain sentences by coordinating with an additional
token-level feature-based retrieval module constructed from in-domain data.
Previous studies have already demonstrated that non-parametric NMT is even
superior to models fine-tuned on out-of-domain data. In spite of this success,
kNN retrieval is at the expense of high latency, in particular for large
datastores. To make it practical, in this paper, we explore a more efficient
kNN-MT and propose to use clustering to improve the retrieval efficiency.
Concretely, we first propose a cluster-based Compact Network for feature
reduction in a contrastive learning manner to compress context features into
90+% lower dimensional vectors. We then suggest a cluster-based pruning
solution to filter out 10%-40% redundant nodes in large datastores while
retaining translation quality. Our proposed methods achieve better or
comparable performance while reducing up to 57% inference latency against the
advanced non-parametric MT model on several machine translation benchmarks.
Experimental results indicate that the proposed methods maintain the most
useful information of the original datastore and the Compact Network shows good
generalization on unseen domains.

本文提出了一种用于领域自适应的非参数翻译解决方案 “k 最近邻机器翻译（kNN-MT）”，并通过在聚类的基础上通过对比学习的方式构建紧凑网络，提高了检索效率并且在大型数据集上取得了更好或相当的性能表现，同时对于不同领域的通用性很强。