  Recent advances in text-to-image synthesis make it possible to visualize
machine imaginations for a given context. On the other hand, when generating
text, human writers are gifted at creative visualization, which enhances their
writings by forming imaginations as blueprints before putting down the stories
in words. Inspired by such a cognitive process, we ask the natural question of
whether we can endow machines with the same ability to utilize visual
information and construct a general picture of the context to guide text
generation. In this work, we propose iNLG that uses machine-generated images to
guide language models in open-ended text generation. The experiments and
analyses demonstrate the effectiveness of iNLG on open-ended text generation
tasks, including text completion, story generation, and concept-to-text
generation in both few-shot and full-data scenarios. Both automatic metrics and
human evaluations verify that the text snippets generated by our iNLG are
coherent and informative while displaying minor degeneration.
