Improving Machine Translation Using an Efficient Dual-Bert Adversarial Network (DBAN) Model for User-Generated Content
TL;DR
An Efficient Dual-BERT Adversarial Network (DBAN) is proposed to improve the translation of noisy UGC by integrating contextual representation learning with adversarial training and significantly improves contextual understanding and cross-lingual semantic alignment while maintaining computational efficiency.
Abstract
Machine Translation (MT) has achieved significant progress with the emergence of Transformer-based Neural Machine Translation (NMT) models. However, translating user-generated content (UGC) remains challenging due to the presence of noisy and informal linguistic features such as abbreviations, spelling errors, slang, emojis, code-switching, and inconsistent grammatical structures. These characteristics often degrade translation quality because conventional NMT systems are primarily trained on clean and well-structured corpora. This study proposes an Efficient Dual-BERT Adversarial Network (DBAN) to improve the translation of noisy UGC by integrating contextual representation learning with adversarial training. The proposed framework employs two pretrained BERT encoders to independently learn contextual representations of source and target sentences, while a cross-attention mechanism enhances semantic alignment between both languages. An adversarial discriminator is incorporated to distinguish authentic target representations from generated representations, encouraging the model to learn robust and domain-invariant semantic features. Furthermore, parameter sharing, knowledge distillation, and layer-freezing techniques are introduced to reduce computational complexity without compromising translation performance. The proposed model was evaluated against Standard Transformer NMT and BERT-enhanced NMT using parallel corpora containing informal user-generated text. Translation performance was assessed using Bilingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Translation Error Rate (TER), and robustness under noisy input conditions. Experimental results demonstrate that the proposed DBAN consistently outperforms the baseline models by producing more accurate translations, preserving semantic meaning more effectively, and exhibiting greater robustness to noisy and domain-diverse user-generated text. The integration of dual contextual encoders and adversarial learning significantly improves contextual understanding and cross-lingual semantic alignment while maintaining computational efficiency. These findings demonstrate that the proposed framework provides a practical and scalable solution for enhancing machine translation of user-generated content and contributes to the development of more robust and context-aware multilingual translation systems suitable for real-world digital communication.