Comparison of dictionary-type RAG and RAG using Japanese and English knowledge graphs in article translation

Abstract

Translating academic texts containing specialized terms not included in the training data of Large Language Models (LLM) presents a significant challenge. We introduce a new approach that utilizes a Retrieval-Augmented Generation (RAG) model with a Japanese-English dictionary and two knowledge graphs constructed from Japanese and English datasets. Experiments with RAGs have demonstrated that dictionary-based RAGs assist in translation. We also explore a RAG approach that leverages two knowledge graphs based on Japanese and English data, revealing that its performance is not inferior. Moreover, we suggest that with an appropriate method of constructing knowledge graphs, this approach could potentially improve translation accuracy.