Japanese waka translation supported by internet of things and artificial intelligence technology - Rizhong Shen (Scientific Reports)
Abstract With the advancement of internet of things (IoT) and artificial intelligence (AI) technology, access to large-scale bilingual parallel data has become more efficient, thereby accelerating the development and application of machine translation. Given the increasing cultural exchanges between China and Japan, many scholars have begun to study the Chinese translation of Japanese waka poetry. Based on this, the study first explores the structure of waka and the current state of its Chinese translations, analyzing existing translation disputes and introducing a data collection method for waka using IoT. Then, an optimized neural machine translation model is proposed, which integrates a Bidirectional Long Short-Term Memory (Bi-LSTM) network, vertical Tree-LSTM, and an attention mechanism into the Transformer framework. Experimental results demonstrate that the three optimized models—Transformer + Bi-LSTM, Transformer + Tree-LSTM, and Transformer + Tree-LSTM + Attention—outperform the baseline Transformer model on both public and waka datasets. The BLEU scores of the models on the public dataset were 23.71, 23.95, and 24.12, respectively. Notably, on the waka dataset, the Transformer + Tree-LSTM + Attention model achieved the highest BLEU score of 20.65, demonstrating a significant advantage in capturing waka’s unique features and contextual information. This study offers new methods to enhance the quality of Chinese-Japanese translation, promoting cultural exchange and understanding.