Year: 2025
Type: article
Abstract: As deep learning is widely used in natural language processing, neural machine translation (NMT) has become mainstream. The attention mechanism offers a new way to enhance translation quality. This paper explores an attention - mechanism model for English translation. By comparing the basic Seq2Seq model and the attention - added model in English - to - Chinese translation tasks, it assesses translation - quality improvement. A simulation experiment using the WMT 2014 English - Chinese parallel corpus is designed, and the model is analyzed with BLEU, ROUGE, and TER. Results show that the attention - based model outperforms the basic one in BLEU and ROUGE scores and has lower TER scores, indicating the attention mechanism effectively boosts translation quality and accuracy. In summary, the attention - based translation model proposed here has good performance and application potential in English translation tasks.
Author: Xiaoli Yu
Institution: Zhejiang Yuexiu University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411680018