Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine
  Translation
Jiayi Wang Ke Wang Yuqi Zhang Yu Zhao Pontus Stenetorp
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.