Data-to-text Generation with Macro Planning

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 th...

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Vydané v:	Transactions of the Association for Computational Linguistics Ročník 9; s. 510 - 527
Hlavní autori:	Puduppully, Ratish, Lapata, Mirella
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.01.2021 MIT Press Journals, The The MIT Press
Predmet:	Automatic text generation Benchmarks Computational linguistics Data Data collection Datasets Encoders-Decoders Experiments Linguistics Neural networks Planners Planning Professional baseball Variants
ISSN:	2307-387X, 2307-387X
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