# Cultural Adaptation of Menus: A Fine-Grained Approach

arXiv: https://arxiv.org/abs/2408.13534 · HTML: https://mlsanity.com/paper/2408.13534/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Investigating Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2402.13231/) (2024)
- [Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions](https://mlsanity.com/paper/2309.12342/) (2023)
- [Benchmarking Vision Language Models for Cultural Understanding](https://mlsanity.com/paper/2407.10920/) (2024)
- [ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://mlsanity.com/paper/2402.12840/) (2024)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [IndoCulture: Exploring Geographically-Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces](https://mlsanity.com/paper/2404.01854/) (2024)
- [Translating Across Cultures: LLMs for Intralingual Cultural Adaptation](https://mlsanity.com/paper/2406.14504/) (2024)
- [Bridging Background Knowledge Gaps in Translation with Automatic Explicitation](https://mlsanity.com/paper/2312.01308/) (2023)
- [The ApposCorpus: A new multilingual, multi-domain dataset for factual appositive generation](https://mlsanity.com/paper/2011.03287/) (2020)
- [Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario](https://mlsanity.com/paper/2210.11431/) (2022)

## Built on by (most cited first, 8 of 8)

- [DIWALI: Diversity and Inclusivity aWare cuLture specific Items for India: Dataset and Assessment of LLMs for Cultural Text Adaptation in Indian Context](https://mlsanity.com/paper/2509.17399/) (2025)
- [CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation](https://mlsanity.com/paper/2505.24456/) (2025)
- [Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation](https://mlsanity.com/paper/2604.24361/) (2026)
- [Evaluating Menu OCR and Translation: A Benchmark for Aligning Human and Automated Evaluations in Large Vision-Language Models](https://mlsanity.com/paper/2504.13945/) (2025)
- [Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translation](https://mlsanity.com/paper/2509.23395/) (2025)
- [To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs](https://mlsanity.com/paper/2601.02858/) (2026)
- [On the Systematic Challenges of Culturally Loaded Machine Translation: Dream of the Red Chamber as the Cultural Lens](https://mlsanity.com/paper/2607.20241/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)

## Abstract (arXiv)

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.