Knowledge Graph-Augmented GNN Encoder with Transformer Decoder for Cross-Lingual Neural Machine Translation: Modeling, Optimization, and Scalable Deployment

Abstract

In cross-language information interaction, translation accuracy and efficiency determine the reliability of multilingual services. This paper proposes a knowledge graph-augmented neural machine translation framework that integrates a 3-layer Graph Attention Network (GAT) encoder with a 6-layer Transformer decoder, fused with XLM-R contextual embeddings. The fusion mechanism injects entity and relation information into decoding, enhancing semantic alignment and long-sentence reasoning. A distributed parallel architecture with cache optimization supports scalable deployment. Experiments on WMT and OpenSubtitles datasets evaluate the model against Transformer and NMT baselines. Results show that compared with the vanilla Transformer and mBART baselines, the proposed system achieves an average BLEU improvement of 17.0%±0.6, a 26.8%±1.5 reduction in PPL, inference time shortened to 0.92s±0.04, and entity alignment error rate reduced to 3.4%±0.3. These results confirm that the performance gains are statistically significant, with confidence intervals reported for all metrics. These findings confirm that knowledge graph augmentation substantially improves translation quality, semantic consistency, real-time performance, and robustness under multilingual and complex contexts. The contributions of this work include: ①a GAT-based encoder for capturing cross-lingual dependencies; ② a fusion method with XLM-R for semantic enhancement; ③ a scalable optimization framework ensuring low-latency translation. This research provides a reproducible and deployable approach for intelligent multilingual interaction and demonstrates significant potential for cross-language applications.