Language Information Processing Algorithms in Intelligent Translation Systems

Intelligent translation systems have key problems such as insufficient multi-language translation accuracy and high real-time requirements. Based on this, this paper combines cross-language word vectors to establish mapping relationships between low-resource languages, and introduces external professional dictionaries to enhance the translation consistency of domain terms. Transformer-based sequence modeling technology is adopted, and multi-head self-attention mechanism and position encoding are used to achieve global context perception. Bayesian optimization and grid search are combined to dynamically adjust hyperparameters, and mixed precision training is used to accelerate convergence. Beam search and length penalty are used to optimize the decoding process, and the reordering strategy is combined to improve the fluency of the target language. Compared with the traditional statistical machine translation (SMT) model, the average BLEU score of the translation system based on the Transformer model is about 93.15%, the TER score is lower, and the translation delay is reduced. This study effectively improves the accuracy and efficiency of translation and meets the needs of cross-language communication in different fields.