  Human brains integrate linguistic and perceptual information simultaneously
to understand natural language, and hold the critical ability to render
imaginations. Such abilities enable us to construct new abstract concepts or
concrete objects, and are essential in involving practical knowledge to solve
problems in low-resource scenarios. However, most existing methods for Natural
Language Understanding (NLU) are mainly focused on textual signals. They do not
simulate human visual imagination ability, which hinders models from inferring
and learning efficiently from limited data samples. Therefore, we introduce an
Imagination-Augmented Cross-modal Encoder (iACE) to solve natural language
understanding tasks from a novel learning perspective -- imagination-augmented
cross-modal understanding. iACE enables visual imagination with external
knowledge transferred from the powerful generative and pre-trained
vision-and-language models. Extensive experiments on GLUE and SWAG show that
iACE achieves consistent improvement over visually-supervised pre-trained
models. More importantly, results in extreme and normal few-shot settings
validate the effectiveness of iACE in low-resource natural language
understanding circumstances.
