Research on Computer-Aided English Translation System Based on Neural Network

The rapid development of neural networks has greatly improved computer-aided English translation systems, but there remain challenges like contextual ambiguity and low accuracy. This study examines a neural network-based method to enhance translation quality with a view to resolving semantic inconsistency and syntactic mistakes. The research utilizes a Bidirectional Long Short-Term Memory (BiLSTM) and attention mechanisms to facilitate contextual understanding. Also, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for dynamic error correction. The Machine Translation dataset is used to train and test, keeping robust performance measures in place. Experimental outcomes prove that the system proposed has better accuracy (98.2%) and perplexity (2.1) than current models while having better fluency and coherence. Novelty arises from the hybrid algorithmic methodology, which tunes both global and local translation contexts, and real-time adaptive learning. Both BLEU (91.7) and METEOR (93.4) performance metrics confirm the system's dominance in managing intricate linguistic structures.