Text intelligent correction in English translation: A study on integrating models with dependency attention mechanism

Improving translation quality and efficiency is one of the key challenges in the field of Natural Language Processing (NLP). This study proposes an enhanced model based on Bidirectional Encoder Representations from Transformers (BERT), combined with a dependency self-attention mechanism, to automatically detect and correct textual errors in the translation process. The model aims to strengthen the understanding of sentence structure, thereby improving both the accuracy and efficiency of error correction. The research uses the Conference on Natural Language Learning (CoNLL)-2014 dataset as an experimental benchmark, which contains a rich collection of grammatical error samples and is a standard resource in linguistic research. During model training, the Adam optimization algorithm is employed, and the model's performance is enhanced by introducing a customized dependency self-attention mechanism for parameter optimization. To validate the model's effectiveness, the performance of the baseline model and the improved model is compared using multiple evaluation metrics, including accuracy, recall, F1 score, edit distance, Bilingual Evaluation Understudy (BLEU) score, and average processing time. The results show that the proposed model significantly outperforms the baseline model in terms of accuracy (improving from 0.78 to 0.85), recall (improving from 0.81 to 0.87), and F1 score (improving from 0.79 to 0.86). The average edit distance decreases from 3.2 to 2.5, the BLEU score increases from 0.65 to 0.72, and the average processing time is reduced from 2.3 seconds to 1.8 seconds. This study provides an innovative approach for intelligent text correction tasks, expands the application scenarios of the BERT model, and offers significant support for the practical implementation of NLP technologies. The findings not only highlight the advantages of the improved model but also offer new ideas and directions for future related research.