  Transformers achieve great performance on Visual Question Answering (VQA).
However, their systematic generalization capabilities, i.e., handling novel
combinations of known concepts, is unclear. We reveal that Neural Module
Networks (NMNs), i.e., question-specific compositions of modules that tackle a
sub-task, achieve better or similar systematic generalization performance than
the conventional Transformers, even though NMNs' modules are CNN-based. In
order to address this shortcoming of Transformers with respect to NMNs, in this
paper we investigate whether and how modularity can bring benefits to
Transformers. Namely, we introduce Transformer Module Network (TMN), a novel
NMN based on compositions of Transformer modules. TMNs achieve state-of-the-art
systematic generalization performance in three VQA datasets, improving more
than 30% over standard Transformers for novel compositions of sub-tasks. We
show that not only the module composition but also the module specialization
for each sub-task are the key of such performance gain.
