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:将相关且准确的信息传达出去对于许多条件生成任务来说至关重要,然而对于神经seq-to-seq模型来说,其输出通常会出现幻觉并且无法正确涵盖重要细节。在这项工作中,我们提出了规划作为使条件生成更少模糊且更实际的有用的中间表示。我们的工作提出了文本计划的一个新概念,作为一系列问答(QA)对的序列。我们使用QA蓝图来增强现有数据集(例如,用于总结)作为内容选择(即要说什么)和计划(即以什么顺序)的代理。我们通过利用最先进的问题生成技术自动获取蓝图,并将输入-输出对转换为输入-蓝图-输出元组。我们开发了基于Transformer的模型,每个模型在生成的输出中以不同方式包含蓝图(例如作为全局计划或迭代地)。在多种度量和数据集的评估中,我们发现相较于那些无需规划就能生成文本的替代方案,蓝图模型更具事实性且允许更紧密地控制生成的输出。