  We introduce a novel framework for evaluating multimodal deep learning models
with respect to their language understanding and generalization abilities. In
this approach, artificial data is automatically generated according to the
experimenter's specifications. The content of the data, both during training
and evaluation, can be controlled in detail, which enables tasks to be created
that require true generalization abilities, in particular the combination of
previously introduced concepts in novel ways. We demonstrate the potential of
our methodology by evaluating various visual question answering models on four
different tasks, and show how our framework gives us detailed insights into
their capabilities and limitations. By open-sourcing our framework, we hope to
stimulate progress in the field of multimodal language understanding.
