  Standard architectures used in instruction following often struggle on novel
compositions of subgoals (e.g. navigating to landmarks or picking up objects)
observed during training. We propose a modular architecture for following
natural language instructions that describe sequences of diverse subgoals. In
our approach, subgoal modules each carry out natural language instructions for
a specific subgoal type. A sequence of modules to execute is chosen by learning
to segment the instructions and predicting a subgoal type for each segment.
When compared to standard, non-modular sequence-to-sequence approaches on
ALFRED, a challenging instruction following benchmark, we find that
modularization improves generalization to novel subgoal compositions, as well
as to environments unseen in training.
