Transfer Learning and Concept Network-based Joint Prediction Model for English Words and Their Capitalization in Neural Machine Translation

This paper addresses the issue of consistency and accuracy degradation in neural machine translation due to English case sensitivity, proposing a word-case joint prediction model based on transfer learning and concept networks. Firstly, a bidirectional Transformer encoder is pre-trained on large-scale monolingual corpora, capturing the coupled distribution of word form and case through masked language modeling. Subsequently, a cross-lingual concept network is constructed, aligning the abstract concept nodes of the source language with the English word form and case patterns, achieving knowledge transfer. In the translation stage, a joint decoder is introduced to simultaneously predict word elements and their case labels, and maintain cross-sentence consistency with the constraints of the concept network. Experimental results on WMT14 English-German, English-French datasets show that the model improves BLEU by 1.8 compared to the baseline, increases case accuracy by 6.3%, and significantly reduces proper nouns and sentence-initial errors. Abandonment analysis confirms that transfer learning and concept constraints are complementary, providing a new idea for precise case control in low-resource scenarios.