  The ability to convey relevant and faithful information is critical for many
tasks in conditional generation and yet remains elusive for neural seq-to-seq
models whose outputs often reveal hallucinations and fail to correctly cover
important details. In this work, we advocate planning as a useful intermediate
representation for rendering conditional generation less opaque and more
grounded. Our work proposes a new conceptualization of text plans as a sequence
of question-answer (QA) pairs. We enhance existing datasets (e.g., for
summarization) with a QA blueprint operating as a proxy for both content
selection (i.e.,~what to say) and planning (i.e.,~in what order). We obtain
blueprints automatically by exploiting state-of-the-art question generation
technology and convert input-output pairs into input-blueprint-output tuples.
We develop Transformer-based models, each varying in how they incorporate the
blueprint in the generated output (e.g., as a global plan or iteratively).
Evaluation across metrics and datasets demonstrates that blueprint models are
more factual than alternatives which do not resort to planning and allow
tighter control of the generation output.
