ALRMR-GEC: Adjusting Learning Rate Based on Memory Rate to Optimize the Edit Scorer for Grammatical Error Correction

Zhixiao Wu, Yao Lu, Jie Wen, Guangming Lu

Abstract: Edit-based approaches for Grammatical Error Correction (GEC) have attracted volume attention due to their outstanding explanations of the correction process and rapid inference. Through exploring the characteristics of the generalized and specific knowledge learning for GEC, we discover that efficiently training GEC systems with satisfactory generalization capacity prefers more generalized knowledge rather than specific knowledge. Current gradient-based methods for training GEC systems, however, usually prioritize minimizing training loss over generalization loss. This paper proposes the strategy of Adjusting Learning Rate Based on Mermory Rate to optimize the edit-based GEC scorer (ALRMR-GEC). Specifically, we introduce the memory rate, a novel metric, to provide an explicit indicator for the model’s state of learning generalized and specific knowledge, which can effectively guide the GEC system to adjust the learning rate timely. Extensive experiments, conducted by optimizing the published edit scorer on the BEA2019 dataset, have shown our ALRMR-GEC significantly enhances the model generalization ability with stable and satisfactory performance nearly irrespective of the initial learning rate selection. Also, our method can accelerate the training over tenfold faster in certain cases. Finally, the experiments indicate the memory rate introduced in our ALRMR-GEC guides the GEC editscorer to learn more generalized knowledge.