Research map / Large language models / Language, safety and interpretability
Large Scaled Relation Extraction With Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train PE extractor with REINFORCE using bag labels instead of supervised bag-label propagation (ablation) | PE CNN extractor · NYT SMALL manual sentence test | accuracy: 13.36% | Helped |
| PE+REINF vs PCNN+ATT | distant supervised RE · NYT LARGE | AUC: 0.259 -> 0.336 | Helped |
| PE+REINF vs APCNNs | distant supervised RE · NYT SMALL | AUC: 0.205 -> 0.252 | Helped |
| add reinforcement learning (ablation) | CNN and PE extractors · NYT SMALL | precision/recall | Helped |
Closest papers by meaning
- A Hierarchical Framework for Relation Extraction with Reinforcement Learning (AAAI 2019 · similarity 0.91)
- Reinforcement Learning for Relation Classification From Noisy Data (AAAI 2018 · similarity 0.91)
- Joint Entity and Relation Extraction with a Hybrid Transformer and Reinforcement Learning Based Model (AAAI 2020 · similarity 0.91)
- SEE: Syntax-Aware Entity Embedding for Neural Relation Extraction (AAAI 2018 · similarity 0.90)
- Enhancing Relation Extraction via Supervised Rationale Verification and Feedback (AAAI 2025 · similarity 0.87)
- Relation-R1: Progressively Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relation Comprehension (AAAI 2026 · similarity 0.86)
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