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    <titleInfo>
        <title>Data-to-text Generation with Macro Planning</title>
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        <namePart type="given">Ratish</namePart>
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    <name type="personal">
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    <abstract>Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events, and their interactions; they are learned from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00381</identifier>
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        <date>2021</date>
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%0 Journal Article
%T Data-to-text Generation with Macro Planning
%A Puduppully, Ratish
%A Lapata, Mirella
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F puduppully-lapata-2021-data
%X Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events, and their interactions; they are learned from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation.
%R 10.1162/tacl_a_00381
%U https://aclanthology.org/2021.tacl-1.31
%U https://doi.org/10.1162/tacl_a_00381
%P 510-527

Markdown (Informal)

[Data-to-text Generation with Macro Planning](https://aclanthology.org/2021.tacl-1.31) (Puduppully & Lapata, TACL 2021)

• Data-to-text Generation with Macro Planning (Puduppully & Lapata, TACL 2021)

ACL

• Ratish Puduppully and Mirella Lapata. 2021. Data-to-text Generation with Macro Planning. Transactions of the Association for Computational Linguistics, 9:510–527.