To denoise DS data, incorporating extra context information has been explored by incorporating knowledge graphs as external information to guide instance selection and adopting multilingual corpora for the information consistency and complementarity.

Columbia University

- Selecting informative instances
- Incorporating extra context information
- Sophisticated mechanisms and training strategies

Methods to alleviate noise from distant supervision for RE data


Some methods adopt multi-instance learning by combining sentences with the same entity pairs and then selecting informative instances from them using graphical models or heuristic strategies. Attention mechanisms can also be designed to highlight informative instances.


Selecting informative instances for denoising DS

Incorporating extra context information for denoising DS

Sophisticated mechanisms and training strategies can be used to enhance distinctly supervised NRE models. Different architectures and training strategies can be combined to construct hybrid frameworks. A soft-label scheme can be adopted by changing unconfident labels during training. Reinforcement learning and adversarial training can also be adopted in DS.


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