@article{4399,
  author = {Chunye Zhang, Tianyue Yu, Yingqi Gao},
  title = {Designing Neural Network-based Intelligent Robot English Translation System},
  journal = {International Journal of Computational Linguistics Research},
  year = {2025},
  volume = {16},
  number = {1},
  doi = {https://doi.org/10.6025/ijclr/2025/16/1/10-17},
  url = {https://www.dline.info/jcl/fulltext/v16n1/jclv16n1_2.pdf},
  abstract = {Under the current wave of artificial intelligence, machine translation is a research direction in natural language
processing, which has significant scientific and practical value. By analyzing the source language text in
depth, we can utilize precise language features such as speech, vocabulary, intonation, etc., to transform it
into understandable language results. Additionally, to address the machine translation task with a massive
amount of language samples, the advantages of annotated data can be leveraged by combining the
characteristics of neural networks to construct a more accurate translation model. Finally, by applying transfer
learning, model overfitting can be avoided, greatly enhancing its generalization performance in less external
input environments.},
}