Exploration and Exploitation: Two Ways to Improve C hinese Spelling Correction Models (2021)

Abstract: A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one. However, CSC models may fail to correct spelling errors covered by the confusion sets, and also will encounter unseen ones. We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances, and apply a task-specific pre-training strategy to enhance the model. The generated adversarial examples are gradually added to the training set. Experimental results show that such an adversarial training method combined with the pre-training strategy can improve both the generalization and robustness of multiple CSC models across three different datasets, achieving state-of-the-art performance for CSC task.

## conclusion in this paper, we have described a character substitution-based method to create large pseudo data to pre-train the models by encouraging them to explore unseen misspellings. we also proposed a data augmentation method for training the csc models by continually adding the adversarial examples, particularly generated to alleviate the weak spot of the current model, to the training set. by the proposed pre-training strategy and adversarial training method, we can pursue both the exploration and exploitation when training the csc models. experimental results demonstrate that the csc models trained with the data augmented by these pseudo data and adversarial examples can substantially be improved in both generalization and robustness.