  Recent pre-trained language models have shown promising capabilities in
generating fluent and realistic natural language text. However, generating
multi-sentence text with global content planning has been a long-existing
research question. Current approaches for controlled text generation can hardly
address this issue, as they usually condition on single known control
attributes. In this study, we propose a low-cost yet effective framework which
explicitly models the global content plan of the generated text. Specifically,
it optimizes the joint distribution of the natural language sequence and the
global content plan in a plug-and-play manner. We conduct extensive experiments
on the well-established Recipe1M+ benchmark. Both automatic and human
evaluations verify that our model achieves the state-of-the-art performance on
the task of recipe generation
