ALICE: Active Learning with Contrastive Natural Language Explanations

Understand

Training a supervised neural network classifier typically requires many annotated training samples.

• Collecting and annotating a large number of data points are costly and sometimes even infeasible.
• Traditional annotation process uses a low-bandwidth human-machine communication interface: classification labels, each of which only provides several bits of information.
• We propose Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning.