  Ordering the selection of training data using active learning can lead to
improvements in learning efficiently from smaller corpora. We present an
exploration of active learning approaches applied to three grounded language
problems of varying complexity in order to analyze what methods are suitable
for improving data efficiency in learning. We present a method for analyzing
the complexity of data in this joint problem space, and report on how
characteristics of the underlying task, along with design decisions such as
feature selection and classification model, drive the results. We observe that
representativeness, along with diversity, is crucial in selecting data samples.
