The threholds vary according to different tasks. 10-60 tokens apply for POS tasks. 350k tokens apply for NLG tasks. 

University of Michigan - Ann Arbor

While it is possible to build English NLP systems for many higher-level applications, low-resource languages lack the data foundation for this. Additionally, even if it is possible to create basic systems for tasks, such as tokenization and named entity recognition, for all tested low-resource languages, the training data is typical of lower quality compared to the English datasets, or very limited in size.

Low-Resource NLP

https://aclanthology.org/2021.naacl-main.201.pdf

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios


1. The availability of task-specific labels.
2. The availability of unlabeled language- or domain-specific text.
3. The availability of auxiliary data.

Low-resource Dimensions in NLP

Low-resource Threholds for NLP

1. Data Augmentation
2. Transfer Learning
3.  Ideas From Low-Resource Machine Learning in Non-NLP Communities


Widely Used Tricks for Low-Resource NLP

For low-resource languages, even monolingual text available on the internet is limited. This scarcity constrains both the quantity and the quality of data that can be mined from the web, which in turn limits the training resources available for building NLP systems in these languages.

Learn Before

Related