  In this paper, we present a state-of-the-art model and introduce a new
dataset for grounded language learning. Our goal is to develop a model that can
learn to follow new instructions given prior instruction-perception-action
examples. We based our work on the SAIL dataset which consists of navigational
instructions and actions in a maze-like environment. The new model we propose
achieves the best results to date on the SAIL dataset by using an improved
perceptual component that can represent relative positions of objects. We also
analyze the problems with the SAIL dataset regarding its size and balance. We
argue that performance on a small, fixed-size dataset is no longer a good
measure to differentiate state-of-the-art models. We introduce SAILx, a
synthetic dataset generator, and perform experiments where the size and balance
of the dataset are controlled.
