Design and Implementation of an Advanced LSTM-based Intelligent Error Correction System for Japanese Translation

The increasing demand for high-quality Japanese language translation for cross-cultural communication has called for smart mechanisms for error correction that can address complicated grammatical structures, contextual subtleties, and ambiguity in sentence formation. Current systems are subject to restricted contextual understanding, the inability to model long-term dependencies, and inefficient management of bidirectional context, resulting in translation errors and loss of fluency. The conventional methods like rule-based correction and elementary sequence-to-sequence models yield marginal improvement but are unable to maintain consistent accuracy across various datasets. The Attention-Enhanced Bidirectional LSTM model in this work combines bidirectional sequence learning with dynamic attention to both identify forward and backward dependencies as well as focusing on the most contextual tokens relevant to error correction. Experimental tests on benchmark Japanese translation corpora prove that the presented model obtains a considerable performance improvement, with 96.8% accuracy and 95.4% F1-score, outperforming traditional LSTM and transformerbased baselines. Its superiority is due to its improved context retention, efficient attention weighting, and strong adaptability to different sentence complexities, yielding more accurate, smooth, and contextually consistent translations.