  Systematic Generalization refers to a learning algorithm's ability to
extrapolate learned behavior to unseen situations that are distinct but
semantically similar to its training data. As shown in recent work,
state-of-the-art deep learning models fail dramatically even on tasks for which
they are designed when the test set is systematically different from the
training data. We hypothesize that explicitly modeling the relations between
objects in their contexts while learning their representations will help
achieve systematic generalization. Therefore, we propose a novel method that
learns objects' contextualized embeddings with dynamic message passing
conditioned on the input natural language and end-to-end trainable with other
downstream deep learning modules. To our knowledge, this model is the first one
that significantly outperforms the provided baseline and reaches
state-of-the-art performance on grounded-SCAN (gSCAN), a grounded natural
language navigation dataset designed to require systematic generalization in
its test splits.
