Summary of Best Of Both Worlds: a Pliable and Generalizable Neuro-symbolic Approach For Relation Classification, by Robert Vacareanu et al.
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
by Robert Vacareanu, Fahmida Alam, Md Asiful Islam, Haris Riaz, Mihai Surdeanu
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: This paper presents a novel neuro-symbolic architecture for relation classification (RC) that leverages the strengths of both rule-based and deep learning methods. The proposed approach combines declarative rules with neural networks to enhance generalization power. Notably, the semantic matcher is trained unsupervised using synthetic data, allowing for rule modifications without retraining the model. Evaluation on two few-shot RC datasets, Few-Shot TACRED and NYT29, shows that the method outperforms previous state-of-the-art models in three out of four settings, despite no human-annotated training data. The approach remains modular and pliable, enabling local rule modifications to improve overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper introduces a new way to classify relationships between things (like “parent” or “employee”). It combines two different approaches: one that uses rules and one that uses artificial intelligence. This combination helps the system learn and adapt better. The system is trained using fake data, which makes it flexible and easy to adjust. In tests on real datasets, this approach outperformed other methods in most cases, even without being trained on any human-labeled data. Additionally, the system allows for local changes to improve performance on specific relationships. |
Keywords
» Artificial intelligence » Classification » Deep learning » Few shot » Generalization » Synthetic data » Unsupervised
