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.

翻译：传递相关且忠实信息的能力对于条件生成的许多任务至关重要，然而神经序列到序列模型仍难以实现这一点，其输出常出现幻觉，且未能正确覆盖重要细节。本研究主张将规划作为有用的中间表示，以减少条件生成的不透明性并增强其基础性。我们提出将文本规划概念化为一系列问答对序列的新方法。通过引入问答蓝图作为内容选择（即说什么）和规划（即按什么顺序说）的代理，我们增强了现有数据集（例如用于摘要的数据集）。我们利用先进的问答生成技术自动获取蓝图，并将输入-输出对转换为输入-蓝图-输出三元组。我们开发了基于Transformer的模型，每种模型在生成输出中融入蓝图的方式各不相同（例如作为全局规划或迭代式规划）。跨指标和数据集的评估表明，与不采用规划的其他方法相比，蓝图模型更具事实性，并能更紧密地控制生成输出。