Data-to-text Generation with Variational Sequential Planning

• URL: http://arxiv.org/abs/2202.13756v1
• Date: Mon, 28 Feb 2022 13:17:59 GMT
• Title: Data-to-text Generation with Variational Sequential Planning
• Authors: Ratish Puduppully and Yao Fu and Mirella Lapata
• Abstract summary: We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input. We propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. We infer latent plans sequentially with a structured variational model, while interleaving the steps of planning and generation.
• Score: 74.3955521225497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input. We focus on generating long-form text, i.e., documents with multiple paragraphs, and propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. We infer latent plans sequentially with a structured variational model, while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Experiments on two data-to-text benchmarks (RotoWire and MLB) show that our model outperforms strong baselines and is sample efficient in the face of limited training data (e.g., a few hundred instances).

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