ALICE: Active Learning with Contrastive Natural Language Explanations

TL;DR

ALICE is a novel active learning framework that leverages contrastive natural language explanations to improve data efficiency in training neural classifiers, reducing the need for large annotated datasets.

Contribution

ALICE introduces a new active learning approach that uses contrastive explanations and semantic parsing to enhance model training with fewer labeled samples.

Findings

Models with ALICE outperform baselines trained with 40-100% more data.

Adding one explanation is equivalent to adding 13-30 labeled data points.

Contrastive explanations significantly improve data efficiency.

Abstract

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. ALICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations from experts. Then it extracts knowledge from these explanations using a semantic parser. Finally, it incorporates the extracted knowledge through dynamically changing the learning model's structure. We applied ALICE in two visual recognition tasks, bird species classification and social relationship classification. We found by incorporating contrastive explanations, our models outperform baseline models that are trained with 40-100% more training data. We found that adding 1 explanation leads to similar performance gain as adding 13-30 labeled training data points.