DeepAI AI Chat
Log In Sign Up

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

by   Jean-François Kagy, et al.

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.


page 1

page 2

page 3

page 4


Evaluating Zero-cost Active Learning for Object Detection

Object detection requires substantial labeling effort for learning robus...

Learning in Confusion: Batch Active Learning with Noisy Oracle

We study the problem of training machine learning models incrementally u...

Human-Like Active Learning: Machines Simulating the Human Learning Process

Although the use of active learning to increase learners' engagement has...

Active Learning with Logged Data

We consider active learning with logged data, where labeled examples are...

Active Learning in Robotics: A Review of Control Principles

Active learning is a decision-making process. In both abstract and physi...

Onception: Active Learning with Expert Advice for Real World Machine Translation

Active learning can play an important role in low-resource settings (i.e...