Conditional Generation with a Question-Answering Blueprint

• URL: http://arxiv.org/abs/2207.00397v2
• Date: Mon, 1 May 2023 09:27:16 GMT
• Title: Conditional Generation with a Question-Answering Blueprint
• Authors: Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, Anders Sandholm, Dipanjan Das, Mirella Lapata
• Abstract summary: We advocate planning as a useful intermediate representation for rendering conditional generation less opaque and more grounded. We obtain blueprints automatically by exploiting state-of-the-art question generation technology. We develop Transformer-based models, each varying in how they incorporate the blueprint in the generated output.
• Score: 84.95981645040281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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