A Context-Aware and Error Correction Model System for English Translation Based on Improved LSTM

To address the issue of reduced English machine translation accuracy due to long-range dependency problems in transmission, this study proposes an LSTM-based English translation intelligent proofreading system that incorporates an attention mechanism. By enhancing the encoding process, which traditionally uses fixed-dimensional vectors in standard LSTM models, an attention mechanism is embedded to create an English translation model focused on long-range context capture. The system then uses an improved phrase translation model within a computer-based intelligent proofreading framework to identify and replace incorrect vocabulary, thereby automating translation proofreading. Experimental results reveal that this design improves translation accuracy by 26.6% and significantly reduces contextual inconsistencies. Compared to similar solutions, the proposed system demonstrates superior accuracy and better contextual coherence in translations.