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        <title>Data-to-text Generation with Variational Sequential Planning</title>
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    <name type="personal">
        <namePart type="given">Ratish</namePart>
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    <name type="personal">
        <namePart type="given">Yao</namePart>
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    <name type="personal">
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    <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, that is, 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).</abstract>
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    <identifier type="doi">10.1162/tacl_a_00484</identifier>
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        <url>https://aclanthology.org/2022.tacl-1.40/</url>
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    <part>
        <date>2022</date>
        <detail type="volume"><number>10</number></detail>
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            <start>697</start>
            <end>715</end>
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%0 Journal Article
%T Data-to-text Generation with Variational Sequential Planning
%A Puduppully, Ratish
%A Fu, Yao
%A Lapata, Mirella
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F puduppully-etal-2022-data
%X 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, that is, 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).
%R 10.1162/tacl_a_00484
%U https://aclanthology.org/2022.tacl-1.40/
%U https://doi.org/10.1162/tacl_a_00484
%P 697-715

Markdown (Informal)

[Data-to-text Generation with Variational Sequential Planning](https://aclanthology.org/2022.tacl-1.40/) (Puduppully et al., TACL 2022)

• Data-to-text Generation with Variational Sequential Planning (Puduppully et al., TACL 2022)

ACL

• Ratish Puduppully, Yao Fu, and Mirella Lapata. 2022. Data-to-text Generation with Variational Sequential Planning. Transactions of the Association for Computational Linguistics, 10:697–715.