READ: Improving Relation Extraction from an ADversarial Perspective
Dawei Li William Hogan Jingbo Shang
Recent works in relation extraction (RE) have achieved promising benchmark
accuracy; however, our adversarial attack experiments show that these works
excessively rely on entities, making their generalization capability
questionable. To address this issue, we propose an adversarial training method
specifically designed for RE. Our approach introduces both sequence- and
token-level perturbations to the sample and uses a separate perturbation
vocabulary to improve the search for entity and context perturbations.
Furthermore, we introduce a probabilistic strategy for leaving clean tokens in
the context during adversarial training. This strategy enables a larger attack
budget for entities and coaxes the model to leverage relational patterns
embedded in the context. Extensive experiments show that compared to various
adversarial training methods, our method significantly improves both the
accuracy and robustness of the model. Additionally, experiments on different
data availability settings highlight the effectiveness of our method in
low-resource scenarios. We also perform in-depth analyses of our proposed
method and provide further hints. We will release our code at
https://github.com/David-Li0406/READ.