Enhancing Consistency in Academic English Writing Feedback Generation with a MacBERT-large Model Combining Adversarial Training and Contrastive Learning

Abstract

Academic English writing feedback generation requires robust semantic understanding, stable feedback output, and accurate discrimination among similar error types. Existing feedback generation systems often produce inconsistent suggestions for semantically equivalent inputs and show limited generalization to complex academic expressions. To improve feedback consistency, this study proposes an enhanced MacBERT-large encoder–decoder model integrating Fast Gradient Method adversarial training and supervised contrastive learning. The MacBERT-large encoder extracts contextual semantic representations of academic text, while a Transformer decoder generates feedback sequences using a dedicated academic vocabulary. FGM adversarial training introduces controlled perturbations into the embedding layer, enabling the model to maintain stable predictions under paraphrased or slightly varied inputs. A supervised contrastive learning module maps text samples into a representation space where feedback cases with the same error type are pulled closer and different error types are separated through NT-Xent loss. A multi-task learning framework jointly optimizes cross-entropy loss, adversarial loss, and contrastive loss to balance generation quality, robustness, and category discrimination. Experiments on the AEW-Feedback dataset containing 15,000 academic papers show that the proposed model achieves 67.3% BLEU-4, 71.2% ROUGE-L, 74.8% METEOR, and a feedback consistency score of 0.891, outperforming MacBERT-large and single-enhancement variants. The method provides a semantic consistency modeling framework for intelligent text generation and academic writing assistance systems.