Network-based Smart Robot English Translation System Using Transfer Learning and End-to-End Training
Zhengkun Yan
International Journal of Computational Linguistics Research, Vol. 16, No. 4, 2025.
DOI: https://doi.org/10.6025/ijclr/2025/16/4/155-163
Link: https://www.dline.info/jcl/fulltext/v16n4/jclv16n4_2.pdf
Abstract:
The paper presents a neural network based intelligent robot English translation system enhanced by transfer learning and end to end training. Recognizing the limitations of traditional rule or statistical based translation methods particularly their inability to handle contextual nuance and language ambiguity the authors leverage deep learning to improve translation accuracy, fluency, and efficiency. The system architecture integrates data collection, preprocessing, neural modeling (including encoder decoder frameworks with attention mechanisms), and user interfaces tailored for human robot interaction. A key innovation is the use of transfer learning, in which a model pre-trained on a high resource English Chinese parallel corpus is adapted to lowresource English contexts, thereby mitigating data scarcity. The study employs Deep Convolutional Neural Networks (DCNNs) with techniques like Batch Normalization and regularization to reduce overfitting and accelerate convergence. Experimental results demonstrate that the model achieves strong performance in translation quality, especially when enhanced with attention mechanisms and domain adaptation strategies. The authors also address challenges such as distribution mismatch between source and target domains and propose solutions like model pruning and dual stream CNNs to improve generalization. Evaluated using standard metrics like BLEU scores and test accuracy, the system shows promise for real world deployment in education, healthcare, and tourism. The research underscores the potential of combining neural machine translation with transfer learning to empower intelligent robots with robust multilingual communication capabilities, while acknowledging the need for larger, more diverse datasets and further architectural refinements.