Optimizing Transformer Models for German Grammar Error Detection and Correction

The demand for advanced language tools is increasing, which highlights the importance of accurate Grammatical Error Detection and Correction (GEDC), especially for complex languages such as German. In this study, a transducer-based optimisation model is constructed to address the problems faced by German grammar, such as morphological variation, flexible word order and grammatical case usage. The model uses linguistic feature embedding, incorporates syntactic biases into the attention mechanism, and introduces a pre-training strategy for German grammatical structures. A dataset of $\mathbf{5 0, 0 0 0}$ sentences labelled with error types such as verb conjugation and case agreement is used to train and evaluate the model. The experimental results show that the proposed framework achieves significant improvements over the baseline model. The F1 score for error detection is $\mathbf{8 7. 5}$ and the BLEU score for error correction is $\mathbf{8 2. 3}$. The error type analysis proves the effectiveness of the framework in coping with a variety of grammatical problems. The ablation study shows that syntactic bias and pre-training strategies make a significant contribution to model performance. These findings demonstrate the potential of language-tailored converter architectures to improve the accuracy and robustness of German GEDC systems. The proposed framework not only facilitates the development of grammar correction techniques for German, but also lays the foundation for the development of other morphologically rich languages.