Domain Event Detection Based on Robust Learning Methods
Aimed at the domain event detection tasks requiring high robustness and lack of high-quali-ty annotated data,a model integrated multiple robust methods and model structures is proposed.First-ly,data cleaning and data augmentation techniques are used to alleviate the problem of significant noise and severe class imbalance in the training set.Then,adversarial learning methods and multiple robust loss functions are attempted to robust learning on noisy data.Finally,integrating multiple pre-training models with pipeline and joint model structures,robust event detection result is obtained.Re-sult shows that the proposed model has strong robustness and high universality under the condition of low-quality training data.
万方数据
维普
