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    <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. The code and dataset are available at https://github.com/Henry8772/ChineseMenuCSI.</abstract>
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%0 Conference Proceedings
%T Cultural Adaptation of Menus: A Fine-Grained Approach
%A Zhang, Zhonghe
%A He, Xiaoyu
%A Iyer, Vivek
%A Birch, Alexandra
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-cultural
%X 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. The code and dataset are available at https://github.com/Henry8772/ChineseMenuCSI.
%U https://aclanthology.org/2024.wmt-1.120
%P 1258-1271

Markdown (Informal)

[Cultural Adaptation of Menus: A Fine-Grained Approach](https://aclanthology.org/2024.wmt-1.120) (Zhang et al., WMT 2024)

• Cultural Adaptation of Menus: A Fine-Grained Approach (Zhang et al., WMT 2024)

ACL

• Zhonghe Zhang, Xiaoyu He, Vivek Iyer, and Alexandra Birch. 2024. Cultural Adaptation of Menus: A Fine-Grained Approach. In Proceedings of the Ninth Conference on Machine Translation, pages 1258–1271, Miami, Florida, USA. Association for Computational Linguistics.