Construction of an automatic German inflection recognition model based on deep neural networks

Abstract

In the context of the increasing demand for cross-border communication and multilingual text processing, the complex word forms such as case, number, gender, and verb conjugation in German pose significant challenges for large-scale automatic analysis. Traditional rule-based and dictionary-driven methods struggle to balance accuracy and generalization capabilities. This paper addresses the task of automatic identification of German word forms and constructs an end-to-end model framework based on deep neural networks. It employs character-level and subword-level dual-channel feature representations, combines encoder-decoder architecture, scaled dot-product attention, and positional encoding, and jointly models long-distance dependencies within words and fine-grained morphological patterns. At the same time, it designs multi-task learning objectives incorporating morphological constraints such as case, number, and gender, and completes model training using the Adam optimizer, learning rate decay, and early stopping strategies. Experimental results show that on the standard test set, the word form accuracy rate reaches 0.917, the character accuracy rate reaches 0.966, and the average edit distance drops to 0.28. In robust scenarios such as 10% character perturbation and domain transfer, the word form accuracy rates remain at 0.887 and 0.868 respectively, with the average edit distance remaining at most 0.43, and the performance decline is within an acceptable range. This has engineering application value for the automatic processing of complex morphological languages and the construction of multilingual natural language processing systems.