低资源语言：过去的工作回顾和未来的挑战
Low-resource Languages: A Review of Past Work and Future Challenges
Tsinghua University（清华大学）
摘要：
NLP目前的一个问题是按摩和处理缺乏有用的训练属性，如监督数据，母语者或专家的数量等的低资源语言，本文简要总结了以前的突破性成就，解决这个问题，并分析了潜在的改进在未来的整体研究方向的背景下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
