Document-Level Neural Machine Translation via Multi-Scale Wavelet Fusion and G-Meshed Transformer with Attention Alignment

Abstract

Document-level neural machine translation (NMT) aims to improve translation coherence by modeling cross-sentence dependencies. However, existing models like the sentence-level Transformer and G-Transformer struggle with capturing global context and produce noisy attention distributions. This paper introduces a novel document-level NMT framework that integrates multi-scale wavelet feature fusion, Balanced Contextual Slicing, a G-Meshed Transformer decoder, and an attention alignment mechanism. The method enhances encoder input using wavelet-derived semantic features, while parity resolution splits documents into overlapping segments to provide richer context without increasing parameters. A mesh structure in the decoder improves feature sharing and weighting across sentences. An attention alignment module further guides the model to focus on semantically relevant context using a lightweight context detector. Experiments on three English-German datasets (TED, News, Europarl) show that our model consistently outperforms strong baselines. In the two-stage training setup, it improves BLEU scores by +0.68 on TED, +0.81 on News, and +1.34 on Europarl over the sentence-level Transformer (average +0.95). With mBART-25 pretraining, it still gains +0.60 BLEU on average over the G-Transformer baseline. The results confirm that our approach significantly improves translation consistency, attention concentration, and handling of discourse phenomena such as deixis and ellipsis.
These results highlight the effectiveness of our framework in enhancing document-level translation consistency, contextual representation, and discourse-level coherence.