CLJun 12, 2020
Low-resource Languages: A Review of Past Work and Future Challenges
arXiv:2006.07264v1238 citations
Originality Synthesis-oriented
AI Analysis
It addresses the problem of enabling NLP for low-resource languages, but is incremental as it reviews existing work.
This review paper summarizes past achievements in processing low-resource languages, which lack training data and resources, and analyzes future research directions for improvements.
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.