Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare both types of models in how much they lend themselves to a particular form of systematic generalization. Using a synthetic VQA test, we evaluate which models are capable of reasoning about all possible object pairs after training on only a small subset of them. Our findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected. We furthermore investigate if modular models that generalize well could be made more end-to-end by learning their layout and parametrization. We find that end-to-end methods from prior work often learn inappropriate layouts or parametrizations that do not facilitate systematic generalization. Our results suggest that, in addition to modularity, systematic generalization in language understanding may require explicit regularizers or priors.

We show that modular structured models are the best in terms of systematic generalization and that their end-to-end versions don't generalize as well. This paper evaluates systemic generalization between modular neural networks and otherwise generic models via introduction of a new, spatial reasoning dataset A targeted empirical evaluation of generalization in models for visual reasoning, focused on the problem of recognizing (object, relation, object) triples in synthetic scenes featuring letters and numbers.