Data-to-text Generation with Variational Sequential Planning

Ratish Puduppully, Yao Fu, Mirella Lapata


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).
Anthology ID:
2022.tacl-1.40
Volume:
Transactions of the Association for Computational Linguistics, Volume 10
Month:
Year:
2022
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
697–715
Language:
URL:
https://aclanthology.org/2022.tacl-1.40
DOI:
10.1162/tacl_a_00484
Bibkey:
Cite (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.
Cite (Informal):
Data-to-text Generation with Variational Sequential Planning (Puduppully et al., TACL 2022)
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PDF:
https://aclanthology.org/2022.tacl-1.40.pdf