  Retrieval-augmented Neural Machine Translation models have been successful in
many translation scenarios. Different from previous works that make use of
mutually similar but redundant translation memories~(TMs), we propose a new
retrieval-augmented NMT to model contrastively retrieved translation memories
that are holistically similar to the source sentence while individually
contrastive to each other providing maximal information gains in three phases.
First, in TM retrieval phase, we adopt a contrastive retrieval algorithm to
avoid redundancy and uninformativeness of similar translation pieces. Second,
in memory encoding stage, given a set of TMs we propose a novel Hierarchical
Group Attention module to gather both local context of each TM and global
context of the whole TM set. Finally, in training phase, a Multi-TM contrastive
learning objective is introduced to learn salient feature of each TM with
respect to target sentence. Experimental results show that our framework
obtains improvements over strong baselines on the benchmark datasets.
