This paper examines the problem of adapting neural machine translation
systems to new, low-resourced languages (LRLs) as effectively and rapidly as
possible. We propose methods based on starting with massively multilingual
"seed models", which can be trained ahead-of-time, and then continuing training
on data related to the LRL. We contrast a number of strategies, leading to a
novel, simple, yet effective method of "similar-language regularization", where
we jointly train on both a LRL of interest and a similar high-resourced
language to prevent over-fitting to small LRL data. Experiments demonstrate
that massively multilingual models, even without any explicit adaptation, are
surprisingly effective, achieving BLEU scores of up to 15.5 with no data from
the LRL, and that the proposed similar-language regularization method improves
over other adaptation methods by 1.7 BLEU points average over 4 LRL settings.
Code to reproduce experiments at this https URL

本文提出了針對低資源語言進行神經機器翻譯系統適應的策略，基於大規模的多語言種子模型，通過將訓練擴展到相關低資源語言數據上，與高資源語言一起進行聯合訓練，實現了相似語言正則化策略，實驗表明在 4 個低資源語言環境下，BLEU 得分平均提高了 1.7。