Cultural Adaptation of Menus: A Fine-Grained Approach

• URL: http://arxiv.org/abs/2408.13534v1
• Date: Sat, 24 Aug 2024 09:25:18 GMT
• Title: Cultural Adaptation of Menus: A Fine-Grained Approach
• Authors: Zhonghe Zhang, Xiaoyu He, Vivek Iyer, Alexandra Birch,
• Abstract summary: 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. We introduce the ChineseMenuCSI dataset, the largest for Chinese-English menu corpora, annotated with CSI vs Non-CSI labels. We develop a novel methodology for automatic CSI identification, which outperforms GPT-based prompts in most categories.
• Score: 58.08115795037042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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