Summary of Read: Improving Relation Extraction From An Adversarial Perspective, by Dawei Li et al.
READ: Improving Relation Extraction from an ADversarial Perspective
by Dawei Li, William Hogan, Jingbo Shang
First submitted to arxiv on: 2 Apr 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 The proposed adversarial training method for relation extraction (RE) improves both the accuracy and robustness of models. The approach introduces sequence- and token-level perturbations, using a separate vocabulary to enhance entity and context understanding. A probabilistic strategy ensures a larger attack budget for entities, allowing models to rely on relational patterns in contexts. Experimental results show significant improvements over various adversarial training methods, particularly in low-resource scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent research has made progress in relation extraction (RE), but the models may not generalize well due to excessive reliance on entities. A new approach to RE tackles this issue by introducing a unique adversarial training method. The technique combines sequence- and token-level perturbations with a separate vocabulary for entity and context understanding. This helps the model focus on relational patterns in contexts, making it more robust. Results show that this approach improves both accuracy and robustness compared to other methods. |
Keywords
» Artificial intelligence » Token
