Transfer Learning and Concept Network-based Joint Prediction Model for English Words and Their Capitalization in Neural Machine Translation
Year: 2025
Type: article
Abstract: 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.
Author: Xianghua Wu
Institution: Zhengzhou University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7103147312
FWCI:
Cites:
Cited by:
Related to: