Low-resource Languages: A Review of Past Work and Future Challenges

TL;DR

This paper reviews past efforts and future challenges in NLP for low-resource languages, highlighting key achievements and identifying areas for further research to improve language processing with limited data.

Contribution

It provides a comprehensive summary of previous work on low-resource languages and discusses future research directions to address existing challenges.

Findings

Summarizes key achievements in low-resource language NLP

Identifies main challenges and gaps for future research

Suggests potential improvements for resource-scarce language processing

Abstract

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.