Context-Aware Prompting for Japanese-Thai Literary Translation in a Low-Resource Setting

Abstract

Japanese-Thai literary translation remains underexplored, particularly in low-resource settings where parallel data and evaluation resources are limited. This paper investigates whether context-aware prompting can improve Japanese-Thai literary translation using large language models. To support this study, we construct JTLitCorpus-80, a Japanese-Thai literary parallel corpus derived from 80 publicly accessible online translated web novels. Using this corpus, we compare eight prompt-based translation settings, including machine translation, post-editing, and context-aware prompting variants with and without dictionary guidance. Results on a 20-title test split show that direct context-aware prompting consistently outperforms machine translation and non-context-aware baselines, while post-editing provides only limited gains once the initial draft is already generated by the same model. We further show that prompt-derived supervision can improve a smaller open model through parameter-efficient adaptation. MQM-based human evaluation supports the automatic-metric trends, indicating that the strongest context-aware setting provides the best overall translation quality. These findings suggest that context-aware prompting is a practical strategy for Japanese-Thai literary translation in low-resource conditions.