Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation

• URL: http://arxiv.org/abs/2209.08738v3
• Date: Tue, 19 Sep 2023 04:25:32 GMT
• Title: Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation
• Authors: Qiang Wang, Rongxiang Weng, Ming Chen
• Abstract summary: kNN-MT successfully incorporates external corpus by retrieving word-level representations at test time. In this work, we highlight that coupling the representations of these two tasks is sub-optimal for fine-grained retrieval. We leverage supervised contrastive learning to learn the distinctive retrieval representation derived from the original context representation.
• Score: 16.558519886325623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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