  A current problem in NLP is massaging and processing low-resource languages
which lack useful training attributes such as supervised data, number of native
speakers or experts, etc. This review paper concisely summarizes previous
groundbreaking achievements made towards resolving this problem, and analyzes
potential improvements in the context of the overall future research direction.
