# The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation

arXiv: https://arxiv.org/abs/1907.00038 · HTML: https://mlsanity.com/paper/1907.00038/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Fluent: An AI Augmented Writing Tool for People who Stutter](https://mlsanity.com/paper/2108.09918/) (2021)
- [Model Uncertainty based Active Learning on Tabular Data using Boosted Trees](https://mlsanity.com/paper/2310.19573/) (2023)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)

## Abstract (arXiv)

We tested in a live setting the use of active learning for selecting text sentences for human annotations used in training a Thai segmentation machine learning model. In our study, two concurrent annotated samples were constructed, one through random sampling of sentences from a text corpus, and the other through model-based scoring and ranking of sentences from the same corpus. In the course of the experiment, we observed the effect of significant changes to the learning environment which are likely to occur in real-world learning tasks. We describe how our active learning strategy interacted with these events and discuss other practical challenges encountered in using active learning in the live setting.