Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer

• URL: http://arxiv.org/abs/2305.13034v2
• Date: Tue, 24 Oct 2023 10:22:05 GMT
• Title: Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer
• Authors: Ruize Gao, Zhirui Zhang, Yichao Du, Lemao Liu, Rui Wang
• Abstract summary: Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success in domain adaptation tasks. We comprehensively analyze $k$NN-MT through theoretical and empirical studies.
• Score: 44.02848852485475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval. However, the reasons underlying its success have not been thoroughly investigated. In this paper, we comprehensively analyze $k$NN-MT through theoretical and empirical studies. Initially, we provide new insights into the working mechanism of $k$NN-MT as an efficient technique to implicitly execute gradient descent on the output projection layer of NMT, indicating that it is a specific case of model fine-tuning. Subsequently, we conduct multi-domain experiments and word-level analysis to examine the differences in performance between $k$NN-MT and entire-model fine-tuning. Our findings suggest that: (1) Incorporating $k$NN-MT with adapters yields comparable translation performance to fine-tuning on in-domain test sets, while achieving better performance on out-of-domain test sets; (2) Fine-tuning significantly outperforms $k$NN-MT on the recall of in-domain low-frequency words, but this gap could be bridged by optimizing the context representations with additional adapter layers.

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