Introduction to Low-Resource NLP
Low-resource NLP refers to techniques and methods for natural language processing (NLP) tasks where the amount of labeled training data is limited.
Many real-world NLP problems involve languages or domains where large, annotated datasets are not readily available. This is often the case with languages other than English, specialized domains like medical text or legal documents, or tasks for which data labeling is expensive and time-consuming.
Techniques for Low-Resource NLP
- Transfer Learning: Leveraging knowledge from a model trained on a resource-rich language to improve performance on a low-resource language.
- Data Augmentation: Creating synthetic data from existing data to increase the size of the training set.
- Zero-Shot Learning: Training a model to perform a task without any labeled examples for that specific task.
- Few-Shot Learning: Training a model with just a small number of labeled examples.