Cultural Adaptation of Menus: A Fine-Grained Approach
<|reference_start|>Cultural Adaptation of Menus: A Fine-Grained Approach: Machine Translation of Culture-Specific Items (CSIs) poses significant challenges. Recent work on CSI translation has shown some success using Large Language Models (LLMs) to adapt to different languages and cultures; however, a deeper analysis is needed to examine the benefits and pitfalls of each method. In this paper, we introduce the ChineseMenuCSI dataset, the largest for Chinese-English menu corpora, annotated with CSI vs Non-CSI labels and a fine-grained test set. We define three levels of CSI figurativeness for a more nuanced analysis and develop a novel methodology for automatic CSI identification, which outperforms GPT-based prompts in most categories. Importantly, we are the first to integrate human translation theories into LLM-driven translation processes, significantly improving translation accuracy, with COMET scores increasing by up to 7 points.<|reference_end|>
@article{zhang2024cultural,
    title={Cultural Adaptation of Menus: A Fine-Grained Approach},
    author={Zhonghe Zhang, Xiaoyu He, Vivek Iyer, Alexandra Birch},
    journal={arXiv preprint arXiv:2408.13534},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13534},
    primaryClass={cs.CL}
}