Research on Long Short-Term Memory Network Model in Corpus-Assisted Translation

This paper proposes a new translation system that combines a large-scale corpus and a deep learning algorithm based on LSTM. First, the LSTM model is trained using bilingual parallel corpora in the corpus to further improve translation accuracy and generalization ability. An optimization strategy based on gradient clipping is adopted to avoid gradient explosion or disappearance and improve training stability. To verify the performance of the model, this paper simulates the model and compares the effects of traditional translation methods and LSTM models. The simulation results show that the LSTM-based translation system shows significant advantages in BLEU score, translation fluency and vocabulary diversity. The BLEU score of the LSTM model on multiple test sets is 5%-10% higher than that of the traditional statistical model. In addition, the training time and translation efficiency of the model have also been optimized to a certain extent, especially in the translation of long texts. The LSTM model can effectively assist the corpus in automatic translation and shows good adaptability and stability in various translation scenarios.