Predicting effective arguments based on hybrid language model
Qingyu Li · 2023
With the rapid development of information equipment, people's demand for teaching aids is increasing. Among them, the construction of writing aid evaluation system is a challenging task, its difficulty lies in the diversity of writing forms and the diversity of context. In this paper, we propose a Hybrid Model based on an ensemble module, which has the characteristics and advantages of both Roberta and Deberta. Our model includes enhanced mask decoder and disentangled attention transformer layers. At the same time, we used FGM adversarial training to improve model performance. We carried out tests on the Predicting Effective Arguments data set, and experiments showed that the performance of our model far exceeded that of other single models, such as Bert model, Roberta model and Deberta model.