Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translation

• URL: http://arxiv.org/abs/2305.13648v1
• Date: Tue, 23 May 2023 03:44:06 GMT
• Title: Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translation
• Authors: Jiayi Wang, Ke Wang, Yuqi Zhang, Yu Zhao, Pontus Stenetorp
• Abstract summary: Non-parametric, k-nearest-neighbor algorithms have recently made inroads to assist generative models such as language models and machine translation decoders. We explore whether such non-parametric models can improve machine translation models at the fine-tuning stage by incorporating statistics from the kNN predictions.
• Score: 22.59222643493867
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Non-parametric, k-nearest-neighbor algorithms have recently made inroads to assist generative models such as language models and machine translation decoders. We explore whether such non-parametric models can improve machine translation models at the fine-tuning stage by incorporating statistics from the kNN predictions to inform the gradient updates for a baseline translation model. There are multiple methods which could be used to incorporate kNN statistics and we investigate gradient scaling by a gating mechanism, the kNN's ground truth probability, and reinforcement learning. For four standard in-domain machine translation datasets, compared with classic fine-tuning, we report consistent improvements of all of the three methods by as much as 1.45 BLEU and 1.28 BLEU for German-English and English-German translations respectively. Through qualitative analysis, we found particular improvements when it comes to translating grammatical relations or function words, which results in increased fluency of our model.

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