What are the features of active learning in NLP tasks?

Active learning in NLP is an approach where the model selects the most informative or uncertain examples for human annotation to effectively improve training quality with limited resources.

Features of active learning in NLP:

• Data selection: The model chooses examples it is most uncertain about (e.g., with low prediction confidence).
• Annotation difficulty: In NLP, annotation can be labor-intensive (e.g., entity annotation or syntactic parsing), so minimizing the number of examples is important.
• Diversity: To avoid overfitting on similar examples, diverse data is selected.
• Interactivity: The process is often iterative — the model trains, selects examples, a human annotates, and the cycle repeats.

Applying active learning can significantly reduce annotation costs and improve model quality with limited data.