Papers › Data-to-text Generation with Variational Sequential Planning
Data-to-text Generation with Variational Sequential Planning
Ratish Puduppully, Yao Fu, Mirella Lapata
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, i.e., 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).
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Data-to-Text Generation | MLB Dataset | SeqPlan | BLEU | 14.29 | #1 of 4 | Archive leaderboard | report |
| Data-to-Text Generation | MLB Dataset (Content Ordering) | SeqPlan | DLD | 22.7 | #1 of 4 | Archive leaderboard | report |
| Data-to-Text Generation | MLB Dataset (Content Selection) | SeqPlan | Precision | 43.3 | #2 of 3 | Archive leaderboard | report |
| Data-to-Text Generation | MLB Dataset (Content Selection) | SeqPlan | Recall | 53.5 | #2 of 3 | Archive leaderboard | report |
| Data-to-Text Generation | MLB Dataset (Relation Generation) | SeqPlan | Precision | 95.9 | #1 of 4 | Archive leaderboard | report |
| Data-to-Text Generation | MLB Dataset (Relation Generation) | SeqPlan | count | 28.9 | #1 of 4 | Archive leaderboard | report |
| Data-to-Text Generation | RotoWire (Relation Generation) | SeqPlan | Precision | 97.6 | #1 of 6 | Archive leaderboard | report |
| Data-to-Text Generation | RotoWire (Relation Generation) | SeqPlan | count | 46.7 | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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