Papers › ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained...
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models
Pierre L. Dognin, Inkit Padhi, Igor Melnyk, Payel Das
Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning. In this paper, we present ReGen, a bidirectional generation of text and graph leveraging Reinforcement Learning (RL) to improve performance. Graph linearization enables us to re-frame both tasks as a sequence to sequence generation problem regardless of the generative direction, which in turn allows the use of Reinforcement Learning for sequence training where the model itself is employed as its own critic leading to Self-Critical Sequence Training (SCST). We present an extensive investigation demonstrating that the use of RL via SCST benefits graph and text generation on WebNLG+ 2020 and TekGen datasets. Our system provides state-of-the-art results on WebNLG+ 2020 by significantly improving upon published results from the WebNLG 2020+ Challenge for both text-to-graph and graph-to-text generation tasks.
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4f1c685480732b74 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Joint Entity and Relation Extraction | TekGen | ReGen-SCST | F1 | 62.3 | #1 of 2 | Archive leaderboard | report |
| Joint Entity and Relation Extraction | TekGen | ReGen-CE | F1 | 61.9 | #2 of 2 | Archive leaderboard | report |
| Joint Entity and Relation Extraction | WebNLG 3.0 | ReGen (Ours) T2G.CE | F1 | 72.3 | #1 of 10 | Archive leaderboard | report |
| Joint Entity and Relation Extraction | WebNLG 3.0 | ReGen (Ours) T2G.RL | F1 | 72 | #4 of 10 | Archive leaderboard | report |
| Joint Entity and Relation Extraction | WebNLG 3.0 | Amazon AI (Shanghai) (guo-etal-2020-2) | F1 | 68.9 | #5 of 10 | Archive leaderboard | report |
| Joint Entity and Relation Extraction | WebNLG 3.0 | bt5 (agarwal-etal-2020-machine) | F1 | 68.2 | #7 of 10 | 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.
Methods
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