  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.
