An Experimental Study on Improved Sequence-to-Sequence Model in Machine Translation

This paper presents the N-Seq2Seq model for enhancing machine translation quality and efficiency.The core innovations include streamlined attention mechanisms for focusing on crucial details, word-level tokenization to preserve meaning, text candidate frames for prediction acceleration, and relative positional encoding reinforcing word associations.Comparative analyses on English-Chinese datasets demonstrate approximately 4 BLEU score improvements over baseline Seq2Seq and 2 BLEU gains over Transformer models.Moreover, the N-Seq2Seq model reduces average inference time by 60% and 43% respectively.These techniques improve contextual modeling, reduce non-essential information, and accelerate reasoning.Importantly, the model achieves higher accuracy with low overhead, making it possible to deploy on mobile applications, while the Chinese-centric design can also be quickly adapted to other languages.