  K-Nearest Neighbor Neural Machine Translation (kNN-MT) successfully
incorporates external corpus by retrieving word-level representations at test
time. Generally, kNN-MT borrows the off-the-shelf context representation in the
translation task, e.g., the output of the last decoder layer, as the query
vector of the retrieval task. In this work, we highlight that coupling the
representations of these two tasks is sub-optimal for fine-grained retrieval.
To alleviate it, we leverage supervised contrastive learning to learn the
distinctive retrieval representation derived from the original context
representation. We also propose a fast and effective approach to constructing
hard negative samples. Experimental results on five domains show that our
approach improves the retrieval accuracy and BLEU score compared to vanilla
kNN-MT.
