Improving English-Chinese translation using the Kolmogorov-Arnold Transformer
Yuzhe Nie · PeerJ Computer Science · 2025
Machine translation is an important part of natural language processing, helping people communicate across languages, localise content, and search for information in different languages. In this article, we introduce a new framework using the Kolmogorov-Arnold Transformer (KAT) to improve translation quality. We test KAT on the Bilingual MELD dataset and compare it with both traditional statistical models and modern neural translation models. Our results show that KAT performs better, achieving a BLEU-4 score of 42.8, a METEOR score of 45.3, and a TER of 40.5, all of which are improvements over standard transformer models. We also find that using a larger vocabulary and adding the Kolmogorov-Arnold network helps improve translation accuracy. These results suggest that Kolmogorov-Arnold-based methods can be a valuable addition to machine translation systems.