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        <title>Improving Quality and Efficiency in Plan-based Neural Data-to-text Generation</title>
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        <namePart type="given">Amit</namePart>
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    <name type="personal">
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            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
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        <name type="personal">
            <namePart type="given">Chenghua</namePart>
            <namePart type="family">Lin</namePart>
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    <abstract>We follow the step-by-step approach to neural data-to-text generation proposed by Moryossef et al (2019), in which the generation process is divided into a text planning stage followed by a plan realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model’s ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module. These extensions result in a generation process that is faster, more fluent, and more accurate.</abstract>
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    <identifier type="doi">10.18653/v1/W19-8645</identifier>
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        <date>2019-oct–nov</date>
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%0 Conference Proceedings
%T Improving Quality and Efficiency in Plan-based Neural Data-to-text Generation
%A Moryossef, Amit
%A Goldberg, Yoav
%A Dagan, Ido
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F moryossef-etal-2019-improving
%X We follow the step-by-step approach to neural data-to-text generation proposed by Moryossef et al (2019), in which the generation process is divided into a text planning stage followed by a plan realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model’s ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module. These extensions result in a generation process that is faster, more fluent, and more accurate.
%R 10.18653/v1/W19-8645
%U https://aclanthology.org/W19-8645
%U https://doi.org/10.18653/v1/W19-8645
%P 377-382

Markdown (Informal)

[Improving Quality and Efficiency in Plan-based Neural Data-to-text Generation](https://aclanthology.org/W19-8645) (Moryossef et al., INLG 2019)

• Improving Quality and Efficiency in Plan-based Neural Data-to-text Generation (Moryossef et al., INLG 2019)

ACL

• Amit Moryossef, Yoav Goldberg, and Ido Dagan. 2019. Improving Quality and Efficiency in Plan-based Neural Data-to-text Generation. In Proceedings of the 12th International Conference on Natural Language Generation, pages 377–382, Tokyo, Japan. Association for Computational Linguistics.