Transfer learning for extremely low resource languages is a challenging task
as there is no large scale monolingual corpora for pre training or sufficient
annotated data for fine tuning. We follow the work of MetaXL which suggests
using meta learning for transfer learning from a single source language to an
extremely low resource one. We propose an enhanced approach which uses multiple
source languages chosen in a data driven manner. In addition, we introduce a
sample selection strategy for utilizing the languages in training by using a
multi armed bandit algorithm. Using both of these improvements we managed to
achieve state of the art results on the NER task for the extremely low resource
languages while using the same amount of data, making the representations
better generalized. Also, due to the method ability to use multiple languages
it allows the framework to use much larger amounts of data, while still having
superior results over the former MetaXL method even with the same amounts of
data.

本研究针对极低资源语言的转移学习提出了多源语言和样本选择策略的增强方法，实现了在使用相同数量数据的情况下 NER 任务的最新成果。