Improving English Machine Translation via Adversarial Transfer Learning–Based Domain Adaptation

This paper investigates the application of adversarial transfer learning in domain-adaptive English machine translation. The approach employs adversarial training to align source and target domain feature spaces, thereby enhancing translation quality in the absence of domain-specific labelled data and mitigating adverse transfer effects. When models trained on one dataset are applied to another with a different distribution, performance loss often occurs; domain adaptation addresses this challenge. Adversarial transfer learning provides a practical solution for ensuring generalisation across domains. Previous studies have explored supervised, semi-supervised, and unsupervised adaptation using adversarial learning, with GAN-based and gradient-reversal methods improving cross-domain translation, though robustness remains limited. The proposed model integrates feature extraction, label classification, and domain discrimination, aligning multiscale fused features through domain-invariant representations. Experimental results demonstrate an accuracy of 98.53%, significantly outperforming baselines such as Auto Gluon (88.75%) and MMD-based methods (85.46%), while also achieving superior F1 scores and reduced GPU time.